Image processing device, image device, image processing method

ABSTRACT

An image including a face is input (S 201 ), a plurality of local features are detected from the input image, a region of a face in the image is specified using the plurality of detected local features (S 202 ), and an expression of the face is determined on the basis of differences between the detection results of the local features in the region of the face and detection results which are calculated in advance as references for respective local features in the region of the face (S 204 ).

This application is a continuation application of Application No.PCT/JP2004/010208, filed Jul. 16, 2004, which claims priority fromJapanese Patent Application No. 2003-199357, filed Jul. 18, 2003,Japanese Patent Application No. 2003-199358, filed Jul. 18, 2003,Japanese Patent Application No. 2004-167588, filed Jun. 4, 2004, andJapanese Patent Application No. 2004-167589, filed Jun. 4, 2004, theentire contents of which are incorporated by reference herein.

TECHNICAL FIELD

The present invention relates to a technique for making discriminationassociated with the category of an object such as a face or the like inan input image.

BACKGROUND ART

Conventionally, in the fields of image recognition and speechrecognition, a recognition processing algorithm specialized to aspecific object to be recognized is implemented by computer software orhardware using a dedicated parallel image processing processor, thusdetecting an object to be recognized.

Especially, some references about techniques for detecting a face as aspecific object to be recognized from an image including the face havebeen conventionally disclosed (for example, see patent references 1 to5).

According to one of these techniques, an input image is searched for aface region using a template called a standard face, and partialtemplates are then applied to feature point candidates such as eyes,nostrils, mouth, and the like to authenticate a person. However, thistechnique is vulnerable to a plurality of face sizes and a change inface direction, since the template is initially used to match the entireface to detect the face region. To solve such problem, a plurality ofstandard faces corresponding to different sizes and face directions mustbe prepared to perform detection. However, the template for the entireface has a large size, resulting in high processing cost.

According to another technique, eye and mouth candidate groups areobtained from a face image, and face candidate groups formed bycombining these groups are collated with a pre-stored face structure tofind regions corresponding to the eyes and mouth. According to thistechnique, the number of faces in the input image is one or a few, theface size is large to some extent, and an image in which a most regionin the input image corresponds to a face, and which has a smallbackground region is assumed as the input image.

According to still another technique, a plurality of eye, nose, andmouth candidates are obtained, and a face is detected on the basis ofthe positional relationship among feature points, which are prepared inadvance.

According to still another technique, upon checking matching levelsbetween shape data of respective parts of a face and an input image, theshape data are changed, and search regions of respective face parts aredetermined based on the previously obtained positional relationship ofparts. With this technique, shape data of an iris, mouth, nose, and thelike are held. Upon obtaining two irises first, and then a mouth, nose,and the like, search regions of face parts such as a mouth, nose, andthe like are limited on the basis of the positions of the irises. Thatis, this algorithm finds the irises (eyes) first in place of parallellydetecting face parts such as irises (eyes), a mouth, nose, and the likethat form a face, and detects face parts such as a mouth and nose usingthe detection result of the irises. This method assumes a case whereinan image includes only one face, and the irises are accurately obtained.If the irises are erroneously detected, search regions of other featuressuch as a mouth, nose, and the like cannot be normally set.

According to still another technique, a region model set with aplurality of determination element acquisition regions is moved in aninput image to determine the presence/absence of each determinationelement within each of these determination element acquisition regions,thus recognizing a face. In this technique, in order to cope with faceswith different sizes or rotated faces, region models with differentsizes and rotated region models must be prepared. If a face with a givensize or a given rotation angle is not present in practice, many wastefulcalculations are made.

Some methods of recognizing an expression of a face in an image havebeen conventionally proposed (for example, see non-patent references 1and 2).

One of these techniques is premised on that partial regions of a faceare visually accurately extracted from a frame image. In anothertechnique, rough positioning of a face pattern is automated, butpositioning of feature points requires visual fine adjustment. In stillanother technique (for example, see patent reference 6), expressionelements are converted into codes using muscle actions, a neural systemconnection relationship, and the like, thus determining an emotion.However, with this technique, regions of parts required to recognize anexpression are fixed, and regions required for recognition are likely tobe excluded or unwanted regions are likely to be included, thusadversely influencing the recognition precision of the expression.

In addition, a system that detects a change corresponding to an ActionUnit of FACS (Facial Action Coding System) known as a method ofobjectively describing facial actions, so as to recognize an expressionhas been examined.

In still another technique (for example, see patent reference 7), anexpression is estimated in real time to deform a three-dimensional (3D)face model, thus reconstructing the expression. With this technique, aface is detected based on a difference image between an input imagewhich includes a face region and a background image which does notinclude any face region, and a chromaticity value indicating a fleshcolor, and the detected face region is then binarized to detect thecontour of the face. The positions of eyes and a mouth are obtained fromthe region within the contour, and a rotation angle of the face iscalculated based on the positions of the eyes and mouth to applyrotation correction. After that, two-dimensional (2D) discrete cosinetransforms are calculated to estimate an expression. The 3D face modelis converted based on a change amount of a spatial frequency component,thereby reconstructing the expression. However, detection of flesh coloris susceptible to variations of illumination and the background. Forthis reason, in this technique, non-detection or erroneous detection ofan object is more likely to occur in the first flesh color extractionprocess.

As a method of identifying a person based on a face image, the Eigenfacemethod (Turk et. al.) is well known (for example, see non-patentreferences 3 and 4). With this method, principal component analysis isapplied to a set of density value vectors of many face images tocalculate orthonormal bases called eigenfaces, and the Karhunen-Loeveexpansion is applied to the density value vector of an input face imageto obtain a dimension-compressed face pattern. The dimension-compressedpattern is used as a feature vector for identification.

As one of methods for identifying a person in practice using the featurevector for identification, the above reference presents a method ofcalculating the distances between the dimension-compressed face patternof an input image and those of persons, which are held, and identifyinga class to which the pattern with the shortest distance belongs as aclass to which the input face image belongs, i.e., a person. However,this method basically uses a corrected image as an input image, which isobtained in such a manner that the position of a face in an image isdetected using an arbitrary method, and the face region undergoes sizenormalization and rotation correction to obtain a face image.

An image processing method that can recognize a face in real time hasbeen disclosed as a prior art (for example, see patent reference 8). Inthis method, an arbitrary region is extracted from an input image, andit is checked if that region corresponds to a face region. If thatregion is a face region, matching between a face image that hasundergone affine transformation and contrast correction, and faces thathave already been registered in a learning database is made to estimatethe probabilities that this is the same person. Based on theprobabilities, a person who is most likely to be the same as the inputface of the registered persons is output.

As one of conventional expression recognition apparatuses, a techniquefor determining an emotion from an expression has been disclosed (forexample, see patent reference 6). An emotion normally expresses afeeling such as anger, grief, and the like. According to the abovetechnique, the following method is available. That is, predeterminedexpression elements are extracted from respective features of a face onthe basis of relevant rules, and expression element information isextracted from the predetermined expression elements. Note that theexpression elements indicate an open/close action of an eye, an actionof a brow, an action of a metope, an up/down action of lips, anopen/close action of the lips, and an up/down action of a lower lip. Theexpression element for a brow action includes a plurality of pieces offacial element information such as the slope of the left brow, that ofthe right brow, and the like.

An expression element code that quantifies the expression element iscalculated from the plurality of pieces of expression elementinformation that form the obtained expression element on the basis ofpredetermined expression element quantization rules. Furthermore, anemotion amount is calculated for each emotion category from thepredetermined expression element code determined for each emotioncategory using a predetermined emotion conversion formula. Then, amaximum value of emotion amounts of each emotion category is determinedas an emotion.

The shapes and lengths of respective features of faces have largedifferences depending on persons. For example, some persons who haveeyes slanting down outwards, narrow eyes, and so forth in theiremotionless images as sober faces, look deceptively joyful fromperceptual viewpoints based on such images, but they are simply keepingtheir faces straight. Furthermore, face images do not always haveconstant sizes and directions of faces. When the face size has varied orthe face has rotated, required feature amounts must be normalized inaccordance with the face size variation or face rotation variation.

When time-series images that assume a daily scene including anon-expression scene as a conversation scene in addition to anexpression scene and a non-expression scene as a sober face image areused as an input image, for example, non-expression scenes such as apronunciation “o” in a conversation scene similar to an expression ofsurprise, pronunciations “i” and “e” similar to expressions of joy, andthe like may be erroneously determined as expression scenes.

-   Patent reference 1: Japanese Patent Laid-Open No. 9-251534-   Patent reference 2: Japanese Patent No. 2767814-   Patent reference 3: Japanese Patent Laid-Open No. 9-44676-   Patent reference 4: Japanese Patent No. 2973676-   Patent reference 5: Japanese Patent Laid-Open No. 11-283036-   Patent reference 6: Japanese Patent No. 2573126-   Patent reference 7: Japanese Patent No. 3062181-   Patent reference 8: Japanese Patent Laid-Open No. 2003-271958-   Non-patent reference 1: G. Donate, T. J. Sejnowski, et. al,    “Classifying Facial Actions” IEEE Trans. PAMI, vol. 21, no. 10,    October 1999-   Non-patent reference 2: Y. Tian, T. Kaneda, and J. F. Cohn    “Recognizing Action Units for Facial Expression Analysis” IEEE tran.    PAMI vol. 23, no. 2, February 2001-   Non-patent reference 3: Shigeru Akamatsu “Computer Facial    Recognition—Survey—”, the Journal of IEICE Vol. 80, No. 8, pp.    2031-2046, August 1997-   Non-patent reference 4: M. Turk, A. Pentland, “Eigenfaces for    recognition” J. Cognitive Neurosci., vol. 3, no. 1, pp. 71-86, March    1991

DISCLOSURE OF INVENTION Problems that Invention is to Solve

The present invention has been made in consideration of theaforementioned problems, and has as its object to provide a techniquefor easily determining a person who has a face in an image, and anexpression of the face.

It is another object of the present invention to cope with variations ofthe position and direction of an object by a simple method in facedetection in an image, expression determination, and personidentification.

It is still another object of the present invention to provide atechnique which is robust against personal differences in facialexpressions, expression scenes, and the like, and can accuratelydetermine the category of an object in an image. It is still anotherobject of the present invention to provide a technique that canaccurately determine an expression even when the face size has varied orthe face has rotated.

Means for Solving Problems

In order to achieve the objects of the present invention, for example,an image processing apparatus of the present invention comprises thefollowing arrangement.

That is, an image processing apparatus is characterized by comprising:

input means for inputting an image including an object;

object region specifying means for detecting a plurality of localfeatures from the image input by the input means, and specifying aregion of the object in the image using the plurality of detected localfeatures; and

determination means for determining a category of the object usingdetection results of the respective local features in the region of theobject specified by the object region specifying means, and detectionresults of the respective local features for an object image which isset in advance as a reference.

In order to achieve the objects of the present invention, for example,an image processing apparatus of the present invention comprises thefollowing arrangement.

That is, an image processing apparatus is characterized by comprising:

input means for successively inputting frame images each including aface;

face region specifying means for detecting a plurality of local featuresfrom the frame image input by the input means, and specifying a regionof a face in the frame image using the plurality of detected localfeatures; and

determination means for determining an expression of the face on thebasis of detection results of the local features detected by the faceregion specifying means in a region of an image of a second frame, as aframe after a first frame, which positionally corresponds to a region ofa face specified by the face region specifying means in an image of thefirst frame input by the input means.

In order to achieve the objects of the present invention, for example,an image processing apparatus of the present invention comprises thefollowing arrangement.

That is, an image processing apparatus is characterized by comprising:

input means for inputting an image including a face;

face region specifying means for detecting a plurality of local featuresfrom the image input by the input means, and specifying a region of aface in the image using the plurality of detected local features;

first determination means for identifying a person who has the face inthe image input by the input means using detection results of the localfeatures in the region of the face detected by the face regionspecifying means, and detection results of the local features which areobtained in advance from images of respective faces; and

second determination means for determining an expression of the faceusing detection results of the local features in the region of the facedetected by the face region specifying means, and detection results ofthe local features for a face image which is set in advance as areference.

In order to achieve the objects of the present invention, for example,an image processing method of the present invention comprises thefollowing arrangement.

That is, an image processing method is characterized by comprising:

an input step of inputting an image including an object;

an object region specifying step of detecting a plurality of localfeatures from the image input in the input step, and specifying a regionof the object in the image using the plurality of detected localfeatures; and

a determination step of determining a category of the object usingdetection results of the respective local features in the region of theobject specified in the object region specifying step, and detectionresults of the respective local features for an object image which isset in advance as a reference.

In order to achieve the objects of the present invention, for example,an image processing method of the present invention comprises thefollowing arrangement.

That is, an image processing method is characterized by comprising:

an input step of successively inputting frame images each including aface;

a face region specifying step of detecting a plurality of local featuresfrom the frame image input in the input step, and specifying a region ofa face in the frame image using the plurality of detected localfeatures; and

a determination step of determining an expression of the face on thebasis of detection results of the local features detected in the faceregion specifying step in a region of an image of a second framesucceeding to a first frame, the region of the image of the second framepositionally corresponds to a region of a face specified in the faceregion specifying step in an image of the first frame input in the inputstep.

In order to achieve the objects of the present invention, for example,an image processing method of the present invention comprises thefollowing arrangement.

That is, an image processing method is characterized by comprising:

an input step of inputting an image including a face;

a face region specifying step of detecting a plurality of local featuresfrom the image input in the input step, and specifying a region of aface in the image using the plurality of detected local features;

a first determination step of identifying a person who has the face inthe image input in the input step using detection results of the localfeatures in the region of the face detected in the face regionspecifying step, and detection results of the local features which areobtained in advance from images of respective faces; and

a second determination step of determining an expression of the faceusing detection results of the local features in the region of the facedetected in the face region specifying step, and detection results ofthe local features for a face image which is set in advance as areference.

In order to achieve the objects of the present invention, for example,an image sensing apparatus according to the present invention, whichcomprises the aforementioned image processing apparatus, ischaracterized by comprising image sensing means for, when an expressiondetermined by the determination means matches a predeterminedexpression, sensing an image input by the input means.

In order to achieve the objects of the present invention, for example,an image processing method of the present invention comprises thefollowing arrangement.

That is, an image processing method is characterized by comprising:

an input step of inputting an image including a face;

a first feature amount calculation step of calculating feature amountsof predetermined portion groups in a face in the image input in theinput step;

a second feature amount calculation step of calculating feature amountsof the predetermined portion groups of a face in an image including theface of a predetermined expression;

a change amount calculation step of calculating change amounts of thefeature amounts of the predetermined portion groups on the basis of thefeature amounts calculated in the first feature amount calculation stepand the feature amounts calculated in the second feature amountcalculation step;

a score calculation step of calculating scores for the respectivepredetermined portion groups on the basis of the change amountscalculated in the change amount calculation step for the respectivepredetermined portion groups; and

a determination step of determining an expression of the face in theimage input in the input step on the basis of the scores calculated inthe score calculation step for the respective predetermined portiongroups.

In order to achieve the objects of the present invention, for example,an image processing method of the present invention comprises thefollowing arrangement.

That is, an image processing apparatus is characterized by comprising:

input means for inputting an image including a face;

first feature amount calculation means for calculating feature amountsof predetermined portion groups in a face in the image input by theinput means;

second feature amount calculation means for calculating feature amountsof the predetermined portion groups of a face in an image including theface of a predetermined expression;

change amount calculation means for calculating change amounts of thefeature amounts of the predetermined portion groups on the basis of thefeature amounts calculated by the first feature amount calculation meansand the feature amounts calculated by the second feature amountcalculation means;

score calculation means for calculating scores for the respectivepredetermined portion groups on the basis of the change amountscalculated by the change amount calculation means for the respectivepredetermined portion groups; and

determination means for determining an expression of the face in theimage input by the input means on the basis of the scores calculated bythe score calculation means for the respective predetermined portiongroups.

In order to achieve the objects of the present invention, for example,an image sensing apparatus of the present invention is characterized bycomprising:

the aforementioned image processing apparatus;

image sensing means for sensing an image to be input to the input means;and

storage means for storing an image determined by the determinationmeans.

Effect of Invention

With the arrangements of the present invention, identification of a facein an image and determination of an expression of the face can be easilymade.

Also, variations of the position and direction of an object can be copedwith by a simple method in face detection in an image, expressiondetermination, and person identification.

Furthermore, the category of an object in an image can be moreaccurately determined by a method robust against personal differences infacial expressions, expression scenes, and the like.

Moreover, even when the face size has varied or the face has rotated, anexpression can be accurately determined.

Other features and advantages of the present invention will becomeapparent from the following description taken in conjunction with theaccompanying drawings. Note that the same reference numerals denote thesame or similar parts throughout the accompanying drawings.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of the specification, illustrate embodiments of the invention and,together with the description, serve to explain the principles of theinvention.

FIG. 1 is a block diagram showing the functional arrangement of an imageprocessing apparatus according to the first embodiment of the presentinvention;

FIG. 2 is a flowchart of a main process for determining a facialexpression in a photographed image;

FIG. 3 is a block diagram showing the functional arrangement of an imageprocessing apparatus according to the second embodiment of the presentinvention;

FIG. 4 is a timing chart showing the operation of the arrangement shownin FIG. 3;

FIG. 5 is a block diagram showing the functional arrangement of an imageprocessing apparatus according to the third embodiment of the presentinvention;

FIG. 6 is a timing chart showing the operation of the arrangement shownin FIG. 5;

FIG. 7A shows primary features;

FIG. 7B shows secondary features;

FIG. 7C shows tertiary features;

FIG. 7D shows a quartic feature;

FIG. 8 is a view showing the arrangement of a neural network used tomake image recognition;

FIG. 9 shows respective feature points;

FIG. 10 is a view for explaining a process for obtaining feature pointsusing primary and tertiary features in the face region shown in FIG. 9;

FIG. 11 is a block diagram showing the basic arrangement of the imageprocessing apparatus according to the first embodiment of the presentinvention;

FIG. 12 is a block diagram showing the arrangement of an example inwhich the image processing apparatus according to the first embodimentof the present invention is applied to an image sensing apparatus;

FIG. 13 is a block diagram showing the functional arrangement of animage processing apparatus according to the fourth embodiment of thepresent invention;

FIG. 14 is a flowchart of a main process for determining a person whohas a face in a photographed image;

FIG. 15A shows a feature vector 1301 used in a personal identificationprocess;

FIG. 15B shows a right-open V-shaped feature detection result of asecondary feature;

FIG. 15C shows a left-open V-shaped feature detection result;

FIG. 15D shows a photographed image including a face region;

FIG. 16 is a table showing data used upon learning in each of threeidentifiers;

FIG. 17 is a block diagram showing the functional arrangement of animage processing apparatus according to the fifth embodiment of thepresent invention;

FIG. 18 is a flowchart of a main process for determining a person whohas a face in a photographed image, and an expression of that face;

FIG. 19 is a table showing an example of the configuration of datamanaged by an integration unit 1708;

FIG. 20 is a block diagram showing the functional arrangement of animage processing apparatus according to the sixth embodiment of thepresent invention;

FIG. 21 is a flowchart of a main process to be executed by the imageprocessing apparatus according to the sixth embodiment of the presentinvention;

FIG. 22 is a table showing an example of the configuration of expressiondetermination data;

FIG. 23 is a block diagram showing the functional arrangement of animage processing apparatus according to the seventh embodiment of thepresent invention;

FIG. 24 is a block diagram showing the functional arrangement of afeature amount calculation unit 6101;

FIG. 25 shows an eye region, cheek region, and mouth region in an edgeimage;

FIG. 26 shows feature points to be detected by a face feature pointextraction section 6113;

FIG. 27 is a view for explaining a “shape of an eye line edge”;

FIG. 28 is a graph to be referred to upon calculating a score from achange amount of an eye edge length as an example of a feature whosechange amount has a personal difference;

FIG. 29 is a graph to be referred to upon calculating a score from achange amount of the length of a distance between the end points of aneye and mouth as a feature whose change amount has no personaldifference;

FIG. 30 is a flowchart of a determination process upon determining usingthe scores for respective feature amounts calculated by a scorecalculation unit 6104 whether or not a facial expression in an inputimage is a “specific expression”;

FIG. 31 is a graph showing an example of the distribution of scorescorresponding to an expression that indicates joy;

FIG. 32 is a block diagram showing the functional arrangement of animage processing apparatus according to the eighth embodiment of thepresent invention;

FIG. 33 is a block diagram showing the functional arrangement of anexpression determination unit 6165;

FIG. 34 is a graph showing the difference between the sum total ofscores and a threshold line while the abscissa plots the image numbersuniquely assigned to time-series images, and the ordinate plots thedifference between the sum total of scores and threshold line, when anon-expression scene as a sober face has changed to a joy expressionscene;

FIG. 35 is a graph showing the difference between the sum total ofscores and threshold line in a conversation scene as a non-expressionscene while the abscissa plots the image numbers of time-series images,and the ordinate plots the difference between the sum total of scoresand threshold line;

FIG. 36 is a flowchart of a process which is executed by an expressionsettlement section 6171 to determine the start timing of an expressionof joy in images successively input from an image input unit 6100;

FIG. 37 is a flowchart of a process which is executed by the expressionsettlement section 6171 to determine the start timing of an expressionof joy in images successively input from the image input unit 6100;

FIG. 38 is a block diagram showing the functional arrangement of animage processing apparatus according to the ninth embodiment of thepresent invention;

FIG. 39 is a block diagram showing the functional arrangement of afeature amount calculation unit 6212;

FIG. 40 shows feature amounts corresponding to respective expressions(expressions 1, 2, and 3) selected by an expression selection unit 6211;

FIG. 41 shows a state wherein the scores are calculated based on changeamounts for respective expressions;

FIG. 42 is a flowchart of a determination process for determining basedon the scores of the shapes of eyes calculated by a score calculationunit whether or not the eyes are closed;

FIG. 43 shows the edge of an eye of a reference face, i.e., that of theeye when the eye is open;

FIG. 44 shows the edge of an eye when the eye is closed;

FIG. 45 is a block diagram showing the functional arrangement of animage processing apparatus according to the 12th embodiment of thepresent invention;

FIG. 46 is a block diagram showing the functional arrangement of afeature amount extraction unit 6701;

FIG. 47 shows the barycentric positions of eyes and a nose in a face inan image;

FIG. 48 shows the barycentric positions of right and left larmiers and anose;

FIG. 49 shows the distance between right and left eyes, the distancesbetween the right and left eyes and nose, and the distance between theeye and nose when no variation occurs;

FIG. 50 shows the distance between right and left eyes, the distancesbetween the right and left eyes and nose, and the distance between theeye and nose when a size variation has occurred;

FIG. 51 shows the distance between right and left eyes, the distancesbetween the right and left eyes and nose, and the distance between theeye and nose when an up/down rotation variation has occurred;

FIG. 52 shows the distance between right and left eyes, the distancesbetween the right and left eyes and nose, and the distance between theeye and nose when a right/left rotation variation has occurred;

FIG. 53 shows the distances between the end points of the right and lefteyes in case of an emotionless face;

FIG. 54 shows the distances between the end points of the right and lefteyes in case of a smiling face;

FIG. 55A is a flowchart of a process for determining a size variation,right/left rotation variation, and up/down rotation variation;

FIG. 55B is a flowchart of a process for determining a size variation,right/left rotation variation, and up/down rotation variation;

FIG. 56 shows the distance between right and left eyes, the distancesbetween the right and left eyes and nose, and the distance between theeye and nose when one of a size variation, right/left rotationvariation, and up/down rotation variation has occurred;

FIG. 57 shows the distance between right and left eyes, the distancesbetween the right and left eyes and nose, and the distance between theeye and nose when a up/down rotation variation and size variation haveoccurred;

FIG. 58 is a flowchart of a process for normalizing feature amounts inaccordance with up/down and right/left rotation variations and sizevariation on the basis of the detected positions of the right and lefteyes and nose, and determining an expression;

FIG. 59 is a block diagram showing the functional arrangement of animage sensing apparatus according to the 13th embodiment of the presentinvention;

FIG. 60 is a block diagram showing the functional arrangement of animage sensing unit 6820;

FIG. 61 is a block diagram showing the functional arrangement of animage processing unit 6821;

FIG. 62 is a block diagram showing the functional arrangement of afeature amount extraction unit 6842;

FIG. 63 is a block diagram showing the functional arrangement of anexpression determination unit 6847; and

FIG. 64 is a block diagram showing the functional arrangement of animage sensing apparatus according to the 14th embodiment of the presentinvention.

BEST MODE FOR CARRYING OUT THE INVENTION

Preferred embodiments of the present invention will be described indetail hereinafter with reference to the accompanying drawings.

First Embodiment

FIG. 1 is a block diagram showing the functional arrangement of an imageprocessing apparatus according to this embodiment. An image processingapparatus according to this embodiment detects a face from an image anddetermines its expression, and comprises an image sensing unit 100,control unit 101, face detection unit 102, intermediate detection resultholding unit 103, expression determination unit 104, image holding unit105, display unit 106, and recording unit 107. Respective units will beexplained below.

The image sensing unit 100 senses an image, and outputs the sensed image(photographed image) to the face detection unit 102, image holding unit105, display unit 106, or recording unit 107 on the basis of a controlsignal from the control unit 101.

The control unit 101 performs processes for controlling the overallimage processing apparatus according to this embodiment. The controlunit 101 is connected to the image sensing unit 100, face detection unit102, intermediate detection result holding unit 103, expressiondetermination unit 104, image holding unit 105, display unit 106, andrecording unit 107, and controls these units so that they operate atappropriate timings.

The face detection unit 102 executes a process for detecting regions offaces in the photographed image (regions of face images included in thephotographed image) from the image sensing unit 101. This process isequivalent to, i.e., a process for obtaining the number of face regionsin the photographed image, the coordinate positions of the face regionsin the photographed images, the sizes of the face regions, and therotation amounts of the face regions in the image (for example, if aface region is represented by a rectangle, a rotation amount indicates adirection and slope of this rectangle in the photographed image). Notethat these pieces of information (the number of face regions in thephotographed image, the coordinate positions of the face regions in thephotographed images, the sizes of the face regions, and the rotationamounts of the face regions in the image) will be generally referred toas “face region information” hereinafter. Therefore, the face regions inthe photographed image can be specified by obtaining the face regioninformation.

These detection results are output to the expression determination unit104. Also, intermediate detection results (to be described later)obtained during the detection process are output to the intermediatedetection result holding unit 103. The intermediate detection resultholding unit 103 holds the intermediate feature detection results.

The expression determination unit 104 receives data of the face regioninformation output from the face detection unit 102 and data of theintermediate feature detection results output from the intermediatedetection result holding unit 103. The expression determination unit 104reads a full or partial photographed image (in case of the partialimage, only an image of the face region) from the image holding unit105, and executes a process for determining an expression of a face inthe read image by a process to be described later.

The image holding unit 105 temporarily holds the photographed imageoutput from the image sensing unit 100, and outputs the full or partialphotographed image held by itself to the expression determination unit104, display unit 106, and recording unit 107 on the basis of a controlsignal from the control unit 101.

The display unit 106 comprises, e.g., a CRT, liquid crystal display, orthe like, and displays the full or partial photographed image outputfrom the image holding unit 105 or a photographed image sensed by theimage sensing unit 100.

The recording unit 107 comprises a device such as one for recordinginformation on a recording medium such as a hard disk drive, DVD-RAM,compact flash (registered trademark), or the like, and records the imageheld by the image holding unit 105 or a photographed image sensed by theimage sensing unit 100.

A main process for determining an expression of a face in a photographedimage, which is executed by the operations of the aforementioned units,will be described below using FIG. 2 which shows the flowchart of thisprocess.

The image sensing unit 100 photographs an image on the basis of acontrol signal from the control unit 101 (step S201). Data of thephotographed image is displayed on the display unit 106, is also outputto the image holding unit 105, and is further input to the facedetection unit 102.

The face detection unit 102 executes a process for detecting a region ofa face in the photographed image using the input photographed image(step S202). The face region detection process will be described in moredetail below.

A series of processes for detecting local features in the photographedimage and specifying a face region will be described below withreference to FIGS. 7A, 7B, 7C, and 7D, in which FIG. 7A shows primaryfeatures, FIG. 7B shows secondary features, FIG. 7C shows tertiaryfeatures, and FIG. 7D shows a quartic feature.

Primary features as the most primitive local features are detectedfirst. As the primary features, as shown in FIG. 7A, a vertical feature701, horizontal feature 702, upward-sloping oblique feature 703, anddownward-sloping oblique feature 704 are to be detected. Note that“feature” represents an edge segment in the vertical direction takingthe vertical feature 701 as an example.

Since a technique for detecting segments in respective directions in thephotographed image is known to those who are skilled in the art,segments in respective directions are detected from the photographedimage using this technique so as to generate an image that has avertical feature alone detected from the photographed image, an imagethat has a horizontal feature alone detected from the photographedimage, an image that has an upward-sloping oblique feature alonedetected from the photographed image, and an image that has adownward-sloping oblique feature alone detected from the photographedimage. As a result, since the sizes (the numbers of pixels in thevertical and horizontal directions) of the four images (primary featureimages) are the same as that of the photographed image, each featureimage and photographed image have one-to-one correspondence betweenthem. In each feature image, pixels of the detected feature assumevalues different from those of the remaining portion. For example, thepixels of the feature assume 1, and those of the remaining portionassume 0. Therefore, if pixels assume a pixel value=1 in the featureimage, it is determined that corresponding pixels in the photographedimage are those which form a primary feature.

By generating the primary feature image group in this way, the primaryfeatures in the photographed image can be detected.

Next, a secondary feature group as combinations of any of the detectedprimary feature group is detected. The secondary feature group includesa right-open V-shaped feature 710, left-open V-shaped feature 711,horizontal parallel line feature 712, and vertical parallel line feature713, as shown in FIG. 7B. The right-open V-shaped feature 710 is afeature defined by combining the upward-slanting oblique feature 703 anddownward-slanting oblique feature 704 as the primary features, and theleft-open V-shaped feature 711 is a feature defined by combining thedownward-slanting oblique feature 704 and upward-slanting obliquefeature 703 as the primary features. Also, the horizontal parallel linefeature 712 is a feature defined by combining the horizontal features702 as the primary features, and the vertical parallel line feature 713is a feature defined by combining the vertical features 701 as theprimary features.

As in generation of the primary feature images, an image that has theright-open V-shaped feature 710 alone detected from the photographedimage, an image that has the left-open V-shaped feature 711 alonedetected from the photographed image, an image that has the horizontalparallel line feature 712 alone detected from the photographed image,and an image that has the vertical parallel line feature 713 alonedetected from the photographed image are generated. As a result, sincethe sizes (the numbers of pixels in the vertical and horizontaldirections) of the four images (secondary feature images) are the sameas that of the photographed image, each feature image and photographedimage have one-to-one correspondence between them. In each featureimage, pixels of the detected feature assume values different from thoseof the remaining portion. For example, the pixels of the feature assume1, and those of the remaining portion assume 0. Therefore, if pixelsassume a pixel value=1 in the feature image, it is determined thatcorresponding pixels in the photographed image are those which form asecondary feature.

By detecting the secondary feature image group in this way, thesecondary features in the photographed image can be generated.

A tertiary feature group as combinations of any features of the detectedsecondary feature group is detected from the photographed image. Thetertiary feature group includes an eye feature 720 and mouth feature721, as shown in FIG. 7C. The eye feature 720 is a feature defined bycombining the right-open V-shaped feature 710, left-open V-shapedfeature 711, horizontal parallel line feature 712, and verticalhorizontal parallel line feature 713 as the secondary features, and themouth feature 721 is a feature defined by combining the right-openV-shaped feature 710, left-open V-shaped feature 711, and horizontalparallel line feature 712 as the secondary features.

As in generation of the primary feature images, an image that has theeye feature 720 alone detected from the photographed image, and an imagethat has the mouth feature 721 alone detected from the photographedimage are generated. As a result, since the sizes (the numbers of pixelsin the vertical and horizontal directions) of the four images (tertiaryfeature images) are the same as that of the photographed image, eachfeature image and photographed image have one-to-one correspondencebetween them. In each feature image, pixels of the detected featureassume values different from those of the remaining portion. Forexample, the pixels of the feature assume 1, and those of the remainingportion assume 0. Therefore, if pixels assume a pixel value=1 in thefeature image, it is determined that corresponding pixels in thephotographed image are those which form a tertiary feature.

By generating the tertiary feature image group in this way, the tertiaryfeatures in the photographed image can be detected.

A quartic feature as a combination of the detected tertiary featuregroup is detected from the photographed image. The quartic feature is aface feature itself in FIG. 7D. The face feature is a feature defined bycombining the eye features 720 and mouth feature 721 as the tertiaryfeatures.

As in generation of the primary feature images, an image that detectsthe face feature (quartic feature image) is generated. As a result,since the size (the numbers of pixels in the vertical and horizontaldirections) of the quartic feature image is the same as that of thephotographed image, the feature image and photographed image haveone-to-one correspondence between them. In each feature image, pixels ofthe detected feature assume values different from those of the remainingportion. For example, the pixels of the feature assume 1, and those ofthe remaining portion assume 0. Therefore, if pixels assume a pixelvalue=1 in the feature image, it is determined that corresponding pixelsin the photographed image are those which form a quartic feature.Therefore, by referring to this quartic feature image, the position ofthe face region can be calculated based on, e.g., the barycentricpositions of pixels with a pixel value=1.

When this face region is specified by a rectangle, a slope of thisrectangle with respect to the photographed image is calculated to obtaininformation indicating the degree and direction of the slope of thisrectangle with respect to the photographed image, thus obtaining theaforementioned rotation amount.

In this way, the face region information can be obtained. The obtainedface region information is output to the expression determination unit104, as described above.

The respective feature images (primary, secondary, tertiary, and quarticfeature images in this embodiment) are output to the intermediatedetection result holding unit 103 as the intermediate detection results.

In this fashion, by detecting the quartic feature in the photographedimage, the region of the face in the photographed image can be obtained.By applying the aforementioned face region detection process to theentire photographed image, even when the photographed image includes aplurality of face regions, respective face regions can be detected.

Note that the face region detection process can also be implementedusing a neural network that attains image recognition by parallelhierarchical processes, and such process is described in M. Matsugu, K.Mori, et. al, “Convolutional Spiking Neural Network Model for RobustFace Detection”, 2002, International Conference On Neural InformationProcessing (ICONIP02).

The processing contents of the neural network will be described belowwith reference to FIG. 8. FIG. 8 shows the arrangement of the neuralnetwork required to attain image recognition.

This neural network hierarchically handles information associated withrecognition (detection) of an object, geometric feature, or the like ina local region of input data, and its basic structure corresponds to aso-called Convolutional network structure (LeCun, Y. and Bengio, Y.,1995, “Convolutional Networks for Images Speech, and Time Series” inHandbook of Brain Theory and Neural Networks (M. Arbib, Ed.), MIT Press,pp. 255-258). The final layer (uppermost layer) can obtain thepresence/absence of an object to be detected, and position informationof that object on the input data if it is present. By applying thisneural network to this embodiment, the presence/absence of a face regionin the photographed image and the position information of that faceregion on the photographed image if it is present are obtained from thefinal layer.

Referring to FIG. 8, a data input layer 801 is a layer for inputtingimage data. A first feature detection layer (1, 0) detects local,low-order features (which may include color component features inaddition to geometric features such as specific direction components,specific spatial frequency components, and the like) at a singleposition in a local region having, as the center, each of positions ofthe entire frame (or a local region having, as the center, each ofpredetermined sampling points over the entire frame) at a plurality ofscale levels or resolutions in correspondence with the number of aplurality of feature categories.

A feature integration layer (2, 0) has a predetermined receptive fieldstructure (a receptive field means a connection range with outputelements of the immediately preceding layer, and the receptive fieldstructure means the distribution of connection weights), and integrates(arithmetic operations such as sub-sampling by means of local averaging,maximum output detection or the like, and so forth) a plurality ofneuron element outputs in identical receptive fields from the featuredetection layer (1, 0). This integration process has a role of allowingpositional deviations, deformations, and the like by spatially blurringthe outputs from the feature detection layer (1, 0). Also, the receptivefields of neurons in the feature integration layer have a commonstructure among neurons in a single layer.

Respective feature detection layers (1, 1), (1, 2), . . . , (1, M) andrespective feature integration layers (2, 1), (2, 2), . . . , (2, M) aresubsequent layers, the former layers ((1, 1), . . . ) detect a pluralityof different features by respective feature detection modules, and thelatter layers ((2, 1), . . . ) integrate detection results associatedwith a plurality of features from the previous feature detection layers.Note that the former feature detection layers are connected (wired) toreceive cell element outputs of the previous feature integration layersthat belong to identical channels. Sub-sampling as a process executed byeach feature integration layer performs averaging and the like ofoutputs from local regions (local receptive fields of correspondingfeature integration layer neurons) from a feature detection cell mass ofan identical feature category.

In order to detect respective features shown in FIGS. 7A, 7B, 7C, and 7Dusing the neural network shown in FIG. 8, the receptive field structureused in detection of each feature detection layer is designed to detecta corresponding feature, thus allowing detection of respective features.Also, receptive field structures used in face detection in the facedetection layer as the final layer are prepared to be suited torespective sizes and rotation amounts, and face data such as the size,direction, and the like of a face can be obtained by detecting which ofreceptive field structures is used in detection upon obtaining theresult indicating the presence of the face.

Referring back to FIG. 2, the control unit 101 checks with reference tothe result of the face region detection process in step S202 by the facedetection unit 102 whether or not a face region is present in thephotographed image (step S203). As this determination method, forexample, whether or not a quartic feature image is obtained is checked.If a quartic feature image is obtained, it is determined that a faceregion is present in the photographed image. In addition, it may bechecked if neurons in the (face) feature detection layer include thatwhich has an output value equal to or larger than a given referencevalue, and it may be determined that a face (region) is present at aposition indicated by a neuron with an output value equal to or largerthan the reference value. If no neuron with an output value equal to orlarger than the reference value is found, it is determined that no faceis present.

If it is determined as a result of the determination process in stepS203 that no face region is present in the photographed image, since theface detection unit 102 advises the control unit 101 accordingly, theflow returns to step S201, and the control unit 101 controls the imagesensing unit 100 to sense a new image.

On the other hand, if a face region is present, since the face detectionunit 102 advises the control unit 101 accordingly, the flow advances tostep S204, and the feature images held in the intermediate detectionresult holding unit 103 are output to the expression determination unit104, which executes a process for determining an expression of a faceincluded in the face region in the photographed image using the inputfeature images and face region information (step S204).

Note that an image to be output from the image holding unit 105 to theexpression determination unit 104 is the entire photographed image.However, the present invention is not limited to such specific image.For example, the control unit 101 may specify a face region in thephotographed image using the face region information, and may output animage of the face region alone to the expression determination unit 104.

The expression determination process executed by the expressiondetermination unit 104 will be described in more detail below. Asdescribed above, in order to detect a facial expression, an Action Unit(AU) used in FACS (Facial Action Coding System) as a general expressiondescription method is detected to perform expression determination basedon the type of the detected AU. AUs include “outer brow raiser”, “lipstretcher”, and the like. Since every expressions of human being can bedescribed by combining AUs, if all AUs can be detected, all expressionscan be determined in principle. However, there are 44 AUs, and it is noteasy to detect all of them.

Hence, in this embodiment, as shown in FIG. 9, end points (B1 to B4) ofbrows, end points (E1 to E4) of eyes, and end points (M1, M2) of a mouthare set as features used in expression determination, and an expressionis determined by obtaining changes of relative positions of thesefeature points. Some AUs can be described by changes of these featurepoints, and a basic expression can be determined. Note that changes ofrespective feature points in respective expressions are held in theexpression determination unit 104 as expression determination data, andare used in the expression determination process of the expressiondetermination unit 104.

FIG. 9 shows respective feature points.

Respective feature points for expression detection shown in FIG. 9 arethe end portions of the eyes, brows, and the like, and the shapes of theend portions are roughly defined by a right-open V shape and left-open Vshape. Hence, these end portions correspond to the right-open V-shapedfeature 710 and left-open V-shaped feature 711 as the secondary featuresshown in, e.g., FIG. 7B.

The feature points used in expression detection have already beendetected in the middle stage of the face detection process in the facedetection unit 102. The intermediate processing results of the facedetection process are held in the intermediate feature result holdingunit 103.

However, the right-open V-shaped feature 710 and left-open V-shapedfeature 711 are present at various positions such as a background andthe like in addition to a face. For this reason, a face region in thesecondary feature image is specified using the face region informationobtained by the face detection unit 102, and the end points of theright-open V-shaped feature 710 and left-open V-shaped feature 711,i.e., those of the brows, eyes, and mouth are detected in this region.

Hence, as shown in FIG. 9, search ranges (RE1, RE2) of the end points ofthe brows and eyes, and a search range (RM) of the end points of themouth are set in the face region. With reference to pixel values withinthe set search ranges, the positions of pixels at the two ends in thehorizontal direction in FIG. 9 of those which form the right-openV-shaped feature 710 and left-open V-shaped feature 711 are detected,and the detected positions are determined as those of the featurepoints. Note that the relative positions of these search ranges (RE1,RE2, RM) with respect to the central position of the face region are setin advance.

For example, since the positions of end pixels in the horizontaldirection in FIG. 9 of those which form the right-open V-shaped feature710 within the search range RE1 are B1 and E1, each of these positionsis set as that of one end of the brow or eye. The positions in thevertical direction of the positions B1 and E1 are referred to, and theupper one of these positions is set as the position of one end of thebrow. In FIG. 9, since B1 is located at a position higher than E1, B1 isset as the position of one end of the brow.

In this manner, the positions of one ends of the eye and brow can beobtained. Likewise, the same process is repeated for the left-openV-shaped feature within the search range RE1, and the positions of B2and E2 of the other ends of the brow and eye can be obtained.

With the above processes, the positions of the two ends of the eyes,brows, and mouth, i.e., the positions of the respective feature pointscan be obtained. Since each feature image has the same size as that ofthe photographed image, and pixels have one-to-one correspondencebetween these images, the positions of the respective feature points inthe feature images can also be used as those in the photographed image.

In this embodiment, the secondary features are used in the process forobtaining the positions of the respective feature points. However, thepresent invention is not limited to this, and one or a combination ofthe primary features, tertiary features, and the like may be used.

For example, in addition to the right-open V-shaped feature 710 andleft-open V-shaped feature 711, the eye feature 720 and mouth feature721 as the tertiary features shown in FIG. 7C, and the vertical feature701, horizontal feature 702, upward-sloping oblique feature 703, anddownward-sloping oblique feature 704 as the primary features can also beused.

A process for obtaining feature points using the primary and tertiaryfeatures will be explained below using FIG. 10. FIG. 10 is a view forexplaining a process for obtaining feature points using the primary andtertiary features in the face region shown in FIG. 9.

As shown in FIG. 10, eye search ranges (RE3, RE4) and a mouth searchrange (RM2) are set, and a range where pixel groups which form the eyefeatures 720 and mouth feature 721 are located is obtained withreference to pixel values within the set search ranges. Then, searchranges (RE5, RE6) of the end points of the brows and eyes and a searchrange (RM3) of the end points of the mouth are set to cover the obtainedrange.

Within each search range (RE5, RE6, RM3), a continuous line segmentformed of the vertical feature 701, horizontal feature 702,upward-sloping oblique feature 703, and downward-sloping oblique feature704 is traced to consequently obtain positions of two ends in thehorizontal direction, thus obtaining the two ends of the eyes, brows,and mouth. Since the primary features are basically results of edgeextraction, regions equal to or higher than a given threshold value areconverted into thin lines for respective detection results, and endpoints can be detected by tracing the conversion results.

The expression determination process using the obtained feature pointswill be described below. In order to eliminate personal differences ofexpression determination, a face detection process is applied to anemotionless face image to obtain detection results of respective localfeatures. Using these detection results, the relative positions ofrespective feature points shown in FIG. 9 or 10 are obtained, and theirdata are held in the expression determination unit 104 as referencerelative positions. The expression determination unit 104 executes aprocess for obtaining changes of respective feature points from thereference positions, i.e., “deviations” with reference to the referencerelative positions and the relative positions of the obtained featurepoints. Since the size of the face in the photographed image is normallydifferent from that of an emotionless face, the positions of therespective feature points are normalized on the basis of the relativepositions of the obtained feature points, e.g., the distance between thetwo eyes.

Then, scores depending on changes of respective feature points arecalculated for respective feature points, and an expression isdetermined based on the distribution of the scores. For example, sincean expression of joy has features: (1) eyes slant down outwards; (2)muscles of cheeks are raised; (3) lip corners are pulled up; and soforth, large changes appear in “the distances from the end points of theeyes to the end points of the mouth”, “the horizontal width of themouth”, and “the horizontal widths of the eyes”. The score distributionobtained from these changes becomes that unique to an expression of joy.

As for the unique score distribution, the same applies to otherexpressions. Therefore, the shape of the distribution is parametricallymodeled by mixed Gaussian approximation to determine a similaritybetween the obtained score distribution and those for respectiveexpressions by checking the distance in a parameter space. An expressionindicated by the score distribution with a higher similarity with theobtained score distribution (the score distribution with a smallerdistance) is determined as an expression of a determination result.

A method of executing a threshold process for the sum total of scoresmay be applied. This threshold process is effective to accuratelydetermine a non-expression scene (e.g., a face that has pronounced “i”during conversation) similar to an expression scene from an expressionscene. Note that one of determination of the score distribution shapeand the threshold process of the sum total may be executed. Bydetermining an expression on the basis of the score distribution and thethreshold process of the sum total of scores, an expression scene can beaccurately recognized, and the detection ratio can be increased.

With the above process, since the expression of the face can bedetermined, the expression determination unit 104 outputs a code (a codeunique to each expression) corresponding to the determined expression.This code may be a number, and its expression method is not particularlylimited.

Next, the expression determination unit 104 checks if the determinedexpression is a specific expression (e.g., smile) which is set inadvance, and notifies the control unit 101 of the determination result(step S205).

If the expression determined by the processes until step S204 is thesame as the specific expression which is set in advance, for example, inthis embodiment, if the “code indicating the expression” output from theexpression determination unit 104 matches a code indicating the specificexpression which is set in advance, the control unit 101 records thephotographed image held by the image holding unit 105 in the recordingunit 107. When the recording unit 107 comprises a DVD-RAM or compactflash (registered trademark), the control unit 101 controls therecording unit 107 to record the photographed image on a storage mediasuch as a DVD-RAM, compact flash (registered trademark), or the like(step S206). An image to be recorded may be an image of the face region,i.e., the face image of the specific expression.

On the other hand, the expression determined by the processes until stepS204 is not the same as the specific expression which is set in advance,for example, in this embodiment, if the “code indicating the expression”output from the expression determination unit 104 does not match a codeindicating the specific expression which is set in advance, the controlunit 101 controls the image sensing unit 100 to sense a new image.

In addition, if the determined expression is the specific expression,the control unit 101 may hold the photographed image on the recordingunit 107 while controlling the image sensing unit 100 to sense the nextimage in step S206. Also, the control unit 101 may control the displayunit 106 to display the photographed image on the display unit 106.

In general, since an expression does not change abruptly but hascontinuity to some extent, if the processes in steps S202 and S204 endwithin a relative short period of time, images continuous to the imagethat shows the specific expression often have the same expressions. Forthis reason, in order to make the face region detected in step S202clearer, the control unit 101 may set photographing parameters (imagesensing parameters of an image sensing system such as exposurecorrection, auto-focus, color correction, and the like) to performphotographing again, and to display and record another image.

FIG. 11 is a block diagram showing the basic arrangement of the imageprocessing apparatus according to this embodiment.

Reference numeral 1001 denotes a CPU, which controls the overallapparatus using programs and data stored in a RAM 1002 and ROM 1003, andexecutes a series of processes associated with expression determinationdescribed above. The CPU 101 corresponds to the control unit 101 in FIG.1.

Reference numeral 1002 denotes a RAM, which comprises an area fortemporarily storing programs and data loaded from an external storagedevice 1007 and storage medium drive 1008, image data input from theimage sensing unit 100 via an I/F 1009, and the like, and also an arearequired for the CPU 1001 to execute various processes. In FIG. 1, theintermediate detection result holding unit 103 and image holding unit105 correspond to this RAM 1002.

Reference numeral 1003 denotes a ROM which stores, e.g., a port program,setup data, and the like of the overall apparatus.

Reference numerals 1004 and 1005 respectively denote a keyboard andmouse, which are used to input various instructions to the CPU 1001.

Reference numeral 1006 denotes a display device which comprises a CRT,liquid crystal display, or the like, and can display various kinds ofinformation including images, text, and the like. In FIG. 1, the displaydevice 1006 corresponds to the display unit 106.

Reference numeral 1007 denotes an external storage device, which servesas a large-capacity information storage device such as a hard disk drivedevice or the like, and saves an OS (operating system), a programexecuted by the CPU 1001 to implement a series of processes associatedwith expression determination described above, and the like. Thisprogram is loaded onto the RAM 1002 in accordance with an instructionfrom the CPU 1001, and is executed by the CPU 1001. Note that thisprogram includes those which correspond to the face detection unit 102and expression determination unit 104 if the face detection unit 102 andexpression determination unit 104 shown in FIG. 1 are implemented byprograms.

Reference numeral 1008 denotes a storage medium drive device 1008, whichreads out programs and data recorded on a storage medium such as aCD-ROM, DVD-ROM, or the like, and outputs them to the RAM 1002 andexternal storage device 1007. Note that a program to be executed by theCPU 1001 to implement a series of processes associated with expressiondetermination described above may be recorded on this storage medium,and the storage medium drive device 1008 may load the program onto theRAM 1002 in accordance with an instruction from the CPU 1001.

Reference numeral 1009 denotes an I/F which is used to connect the imagesensing unit 100 shown in FIG. 1 and this apparatus. Data of an imagesensed by the image sensing unit 100 is output to the RAM 1002 via theI/F 1009.

Reference numeral 1010 denotes a bus which interconnects theaforementioned units.

A case will be explained below with reference to FIG. 12 wherein theimage processing apparatus according to this embodiment is mounted in animage sensing apparatus, which senses an image when an object has aspecific expression. FIG. 12 is a block diagram showing the arrangementof an example in which the image processing apparatus according to thisembodiment is used in an image sensing apparatus.

An image sensing apparatus 5101 shown in FIG. 12 comprises an imagingoptical system 5102 including a photographing lens and zoomphotographing drive control mechanism, a CCD or CMOS image sensor 5103,a measurement unit 5104 of image sensing parameters, a video signalprocessing circuit 5105, a storage unit 5106, a control signalgeneration unit 5107 for generating control signals used to control animage sensing operation, image sensing conditions, and the like, adisplay 5108 which also serves as a viewfinder such as an EVF or thelike, a strobe emission unit 5109, a recording medium 5110, and thelike, and further comprises the aforementioned image processingapparatus 5111 as an expression detection apparatus.

This image sensing apparatus 5101 performs detection of a face image ofa person (detection of a position, size, and rotation angle) anddetection of an expression from a sensed video picture using the imageprocessing apparatus 5111. When the position information, expressioninformation, and the like of that person are input from the imageprocessing apparatus 5111 to the control signal generation unit 5107,the control signal generation unit 5107 generates a control signal foroptimally photographing an image of that person on the basis of theoutput from the image sensing parameter measurement unit 5104. Morespecifically, the photographing timing can be set when the full-facedimage of the person is obtained at the center of the photographingregion to have a predetermined size or more, and the person smiles.

When the aforementioned image processing apparatus is used in the imagesensing apparatus in this way, face detection and expression detection,and a timely photographing operation based on these detection resultscan be made. In the above description, the image sensing apparatus 5101which comprises the aforementioned processing apparatus as the imageprocessing apparatus 5111 has been explained. Alternatively, theaforementioned algorithm may be implemented as a program, and may beinstalled in the image sensing apparatus 5101 as processing meansexecuted by the CPU.

An image processing apparatus which can be applied to this image sensingapparatus is not limited to that according to this embodiment, and imageprocessing apparatuses according to embodiments to be described belowmay be applied.

As described above, since the image processing apparatus according tothis embodiment uses local features such as the primary features,secondary features, and the like, not only a face region in thephotographed image can be specified, but also an expressiondetermination process can be done more simply without any new detectionprocesses of a mouth, eyes, and the like.

Even when the positions, directions, and the like of faces inphotographed images are all different, the aforementioned local featurescan be obtained, and the expression determination process can be doneconsequently. Therefore, expression determination robust against thepositions, directions, and the like of faces in images can be attained.

According to this embodiment, during a process for repeatingphotographing, only a specific expression can be photographed.

Note that an image used to detect a face region in this embodiment is aphotographed image. However, the present invention is not limited tosuch specific image, and an image which is saved in advance ordownloaded may be used.

Second Embodiment

In this embodiment, the detection process of a face detection region(step S202) and the expression determination process (step S204) in thefirst embodiment are parallelly executed. In this manner, the overallprocess can be done at higher speed.

FIG. 3 is a block diagram showing the functional arrangement of an imageprocessing apparatus according to this embodiment. In the arrangementaccording to this embodiment, the arrangement of an intermediatedetection result holding unit 303 and that of an image holding unit 305are substantially different from those according to the firstembodiment.

The intermediate detection result holding unit 303 further comprisesintermediate detection result holding sections A 313 and B 314.Likewise, the image holding unit 305 comprises image holding sections A315 and B 316.

The operation of the arrangement shown in FIG. 3 will be described belowusing the timing chart of FIG. 4.

In the timing chart of FIG. 4, “A” indicates an operation in an A mode,and “B” indicates an operation in a B mode. The A mode of “imagephotographing” is to hold a photographed image in the image holdingsection A 315 upon holding it in the image holding unit 305, and the Bmode is to hold an image in the image holding section B 316. The A and Bmodes of image photographing are alternately switched, and an imagesensing unit 300 photographs images accordingly. Hence, the imagesensing unit 300 successively photographs images. Note that thephotographing timings are given by a control unit 101.

The A mode of “face detection” is to hold intermediate processingresults in the intermediate detection result holding section A 313 uponholding them in the intermediate detection result holding unit 303 in aface region detection process of a face detection unit 302, and the Bmode is to hold the results in the intermediate detection result holdingsection B 314.

The A mode of “expression determination” is to determine an expressionusing the image held in the image holding section A 315, theintermediate processing results held in the intermediate detectionresult holding section A 313, and face region information of a facedetection unit 302 in an expression determination process of anexpression determination unit 304, and the B mode is to determine anexpression using the image held in the image holding section B 316, theintermediate processing results held in the intermediate detectionresult holding section B 314, and face region information of the facedetection unit 302.

The operation of the image processing apparatus according to thisembodiment will be described below.

An image is photographed in the A mode of image photographing, and thephotographed image is held in the image holding section A 315 of theimage holding unit 305. Also, the image is displayed on a display unit306, and is input to the face detection unit 302. The face detectionunit 302 executes a process for generating face region information byapplying the same process as in the first embodiment to the input image.If a face is detected from the image, data of the face regioninformation is input to the expression determination unit 304.Intermediate feature detection results obtained during the facedetection process are held in the intermediate detection result holdingsection A 313 of the intermediate result holding unit 303.

Next, the image photographing process and face detection process in theB mode and the expression determination process in the A mode areparallelly executed. In the image photographing process in the B mode, aphotographed image is held in the image holding section B 316 of theimage holding unit 305. Also, the image is displayed on the display unit306, and is input to the face detection unit 302. The face detectionunit 302 executes a process for generating face region information byapplying the same process as in the first embodiment to the input image,and holds intermediate processing results in the intermediate processingresult holding section B 314.

Parallel to the image photographing process and face region detectionprocess in the B mode, the expression determination process in the Amode is executed. In the expression determination process in the A mode,the expression determination unit 304 determines an expression of a faceusing the face region information from the face detection unit 302 andthe intermediate feature detection results held in the intermediatedetection result holding section A 313 with respect to the image inputfrom the image holding section A 315. If the expression determined bythe expression determination unit 304 matches a desired expression, theimage in the image holding section A 315 is recorded, thus ending theprocess.

If the expression determined by the expression determination unit 304 isdifferent from a desired expression, the image photographing process andface region detection process in the A mode, and the expressiondetermination process in the B mode are parallelly executed. In theimage photographing process in the A mode, a photographed image is heldin the image holding section A 315 of the image holding unit 305. Also,the image is displayed on the display unit 306, and is input to the facedetection processing unit 302. The face detection unit 302 applies aface region detection process to the input image. In the expressiondetermination process in the B mode, which is done parallel to theaforementioned processes, the expression determination unit 304 detectsan expression of a face using the face region information from the facedetection unit 302 and the intermediate detection results held in theintermediate detection result holding section B 314 with respect to theimage input from the image holding section B 316.

The same processes are repeated until it is determined that theexpression determined by the expression determination unit 304 matches aspecific expression. When the desired expression is determined, if thecurrent expression determination process is the A mode, the image of theimage holding section A 315 is recorded, or if it is the B mode, theimage of the image holding section B 316 is recorded, thus ending theprocess.

Note that the modes of the respective processes are switched by thecontrol unit 101 at a timing when the control unit 101 detectscompletion of the face detection process executed by the face detectionunit 102.

In this manner, since the image holding unit 305 comprises the imageholding sections A 315 and B 316, and the intermediate detection resultholding unit 303 comprises the intermediate detection result holdingsections A 313 and B 314, the image photographing process, face regiondetection process, and expression determination process can beparallelly executed. As a result, the photographing rate of images usedto determine an expression can be increased.

Third Embodiment

An image processing apparatus according to this embodiment has as itsobject to improve the performance of the whole system by parallellyexecuting the face region detection process executed by the facedetection unit 102 and the expression determination process executed bythe expression determination unit 104 in the first and secondembodiments.

In the second embodiment, by utilizing the fact that the imagephotographing and face region detection processes require a longeroperation time than the expression determination process, the expressiondetermination process, and the photographing process and face regiondetection process of the next image are parallelly executed. Bycontrast, in this embodiment, by utilizing the face that the process fordetecting a quartic feature amount shown in FIG. 7D in the firstembodiment requires a longer processing time than detection of tertiaryfeature amounts from primary feature amounts, face region informationutilizes the detection results of the previous image, and the featurepoint detection results used to detect an expression of eyes and a mouthutilize the detection results of the current image. In this way,parallel processes of the face region detection process and expressiondetermination process are implemented.

FIG. 5 is a block diagram showing the functional arrangement of theimage processing apparatus according to this embodiment.

An image sensing unit 500 senses a time-series image or moving image andoutputs data of images of respective frames to a face detection unit502, image holding unit 505, display unit 506, and recording unit 507.In the arrangement according to this embodiment, the face detection unit502 and expression determination unit 504 are substantially differentfrom those of the first embodiment.

The face detection unit 502 executes the same face region detectionprocess as that according to the first embodiment. Upon completion ofthis process, the unit 502 outputs an end signal to the expressiondetermination unit 504.

The expression determination unit 504 further includes a previous imagedetection result holding section 514.

The processes executed by the respective units shown in FIG. 5 will beexplained below using the timing chart shown in FIG. 6.

When the image sensing unit 500 photographs an image of the first frame,data of this image is input to the face detection unit 502. The facedetection unit 502 generates face region information by applying thesame process as in the first embodiment to the input image, and outputsthe information to the expression determination unit 504. The faceregion information input to the expression determination unit 504 isheld in the previous image detection result holding section 514. Also,intermediate feature detection results obtained during the process ofthe unit 502 are input to and held by the intermediate detection resultholding unit 503.

When the image sensing unit 500 photographs an image of the next frame,data of this image is input to the face detection unit 502. Thephotographed image is displayed on the display unit 506, and is alsoinput to the face detection unit 502. The face detection unit 502generates face region information by executing the same process as inthe first embodiment. Upon completion of this face region detectionprocess, the face detection unit 502 inputs intermediate featuredetection results to the intermediate detection result holding unit 503,and outputs a signal indicating completion of a series of processes tobe executed by the expression determination unit 504.

If an expression as a determination result of the expressiondetermination unit 504 is not a desired expression, the face regioninformation obtained by the face detection unit 502 is held in theprevious image detection result holding section 514 of the expressiondetermination unit 504.

Upon reception of the end signal from the face detection unit 502, theexpression determination unit 504 executes an expression determinationprocess for the current image using face region information 601 for theprevious image (one or more images of previous frames) held in theprevious image detection result holding section 514, the current image(image of the current frame) held in the image holding unit 505, andintermediate feature detection results 602 of the current image held inthe intermediate detection result holding unit 503.

In other words, the unit 504 executes the expression determinationprocess for a region in an original image corresponding in position to aregion specified by the face region information in one or more images ofprevious frames using the intermediate detection results obtained fromthat region.

If the difference between the photographing times of the previous imageand current image is short, the positions of face regions in respectiveimages do not change largely. For this reason, the face regioninformation obtained from the previous image is used, and broader searchranges shown in FIGS. 9 and 10 are set, thus suppressing the influenceof positional deviation between the face regions of the previous andcurrent images upon execution of the expression determination process.

If the expression determined by the expression determination unit 504matches a desired expression, the image of the image holding unit 505 isrecorded, thus ending this process. If the expression determined by theexpression determination unit 504 is different from a desiredexpression, the next image is photographed, the face detection unit 502executes a face detection process, and the expression determination unit504 executes an expression determination process using the photographedimage, the face detection result for the previous image held in theprevious image detection result holding section 514, and theintermediate processing results held in the intermediate detectionresult holding unit 503.

The same processes are repeated until an expression determined by theexpression determination unit 504 matches a desired expression. If adesired expression is determined, the image of the image holding unit505 is recorded, thus ending the process.

Since the expression determination process is executed using the faceregion information for the previous image held in the previous imagedetection result holding section 514 and the intermediate featuredetection process results held in the intermediate detection resultholding unit 503, the face region detection process and expressiondetermination process can be parallelly executed. As a result, thephotographing rate of images used to determine an expression can beincreased.

Fourth Embodiment

In the above embodiments, the technique for determining a facialexpression has been explained. In this embodiment, a technique fordetermining a person who has that face, i.e., for identifying a personcorresponding to the face, will be described.

FIG. 13 is a block diagram showing the functional arrangement of animage processing apparatus according to this embodiment. An imageprocessing apparatus according to this embodiment comprises an imagesensing unit 1300, control unit 1301, face detection unit 1302,intermediate detection result holding unit 1303, personal identificationunit 1304, image holding unit 1305, display unit 1306, and recordingunit 1307. The respective units will be described below.

The image sensing unit 1300 senses an image, and outputs the sensedimage (photographed image) to the face detection unit 1302, imageholding unit 1305, and display unit 1306 or recording unit 1307 on thebasis of a control signal from the control unit 1301.

The control unit 1301 performs processes for controlling the overallimage processing apparatus according to this embodiment. The controlunit 1301 is connected to the image sensing unit 1300, face detectionunit 1302, intermediate detection result holding unit 1303, personalidentification unit 1304, image holding unit 1305, display unit 1306,and recording unit 107, and controls these units so that they operate atappropriate timings.

The face detection unit 1302 executes a process for detecting regions offaces in the photographed image (regions of face images included in thephotographed image) from the image sensing unit 1301. This process is,i.e., a process for determining the presence/absence of face regions inthe photographed image, and obtaining, if face regions are present, thenumber of face regions in the photographed image, the coordinatepositions of the face regions in the photographed images, the sizes ofthe face regions, and the rotation amounts of the face regions in theimage (for example, if a face region is represented by a rectangle, arotation amount indicates a direction and slope of this rectangle in thephotographed image). Note that these pieces of information (the numberof face regions in the photographed image, the coordinate positions ofthe face regions in the photographed images, the sizes of the faceregions, and the rotation amounts of the face regions in the image) willbe generally referred to as “face region information” hereinafter.Therefore, the face regions in the photographed image can be specifiedby obtaining the face region information.

These detection results are output to the personal identification unit1304. Also, intermediate detection results (to be described later)obtained during the detection process are output to the intermediatedetection result holding unit 1303.

The intermediate detection result holding unit 1303 holds theintermediate feature detection results output from the face detectionunit 1302.

The personal identification unit 1304 receives data of the face regioninformation output from the face detection unit 1302 and data of theintermediate feature detection results output from the intermediatedetection result holding unit 1303. The personal identification unit1304 executes a determination process for determining a person who hasthis face on the basis of these data. This determination process will bedescribed in detail later.

The image holding unit 1305 temporarily holds the photographed imageoutput from the image sensing unit 1300, and outputs the full or partialphotographed image held by itself to the display unit 1306 and recordingunit 107 on the basis of a control signal from the control unit 1301.

The display unit 1306 comprises, e.g., a CRT, liquid crystal display, orthe like, and displays the full or partial photographed image outputfrom the image holding unit 1305 or a photographed image sensed by theimage sensing unit 1300.

The recording unit 107 comprises a device such as one for recordinginformation on a recording medium such as a hard disk drive, DVD-RAM,compact flash (registered trademark), or the like, and records the imageheld by the image holding unit 1305 or a photographed image sensed bythe image sensing unit 1300.

A main process for determining a person who has a face in a photographedimage, which is executed by the operations of the aforementioned units,will be described below using FIG. 14 which shows the flowchart of thisprocess.

The image sensing unit 1300 photographs an image on the basis of acontrol signal from the control unit 1301 (step S1401). Data of thephotographed image is displayed on the display unit 1306, is also outputto the image holding unit 1305, and is further input to the facedetection unit 1302.

The face detection unit 1302 executes a process for detecting a regionof a face in the photographed image using the input photographed image(step S1402). Since the face region detection process is done in thesame manner as in the first embodiment, a description thereof will beomitted. As a large characteristic feature of the face detectionprocessing system according to this embodiment, features such as eyes, amouth, the end points of the eyes and mouth, and the like, which areeffective for person identification are detected.

The control unit 1301 checks with reference to the result of the faceregion detection process in step S1402 by the face detection unit 1302whether or not a face region is present in the photographed image (stepS1403). As this determination method, for example, it is checked ifneurons in the (face) feature detection layer include that which has anoutput value equal to or larger than a given reference value, and it isdetermined that a face (region) is present at a position indicated by aneuron with an output value equal to or larger than the reference value.If no neuron with an output value equal to or larger than the referencevalue is found, it is determined that no face is present.

If it is determined as a result of the determination process in stepS1403 that no face region is present in the photographed image, sincethe face detection unit 1302 advises the control unit 1301 accordingly,the flow returns to step S1401, and the control unit 1301 controls theimage sensing unit 1300 to sense a new image.

On the other hand, if a face region is present, since the face detectionunit 1302 advises the control unit 1301 accordingly, the flow advancesto step S1404, and the control unit 1301 controls the intermediatedetection result holding unit 1303 to hold the intermediate detectionresult information of the face detection unit 1302, and inputs the faceregion information of the face detection unit 1302 to the personalidentification unit 1304.

Note that the number of faces can be obtained based on the number ofneurons with output values equal to or larger than the reference value.Face detection by means of the neural network is robust against facesize and rotation variations. Hence, one face in an image does notalways correspond to one neuron that has an output value exceeding thereference value. In general, one face corresponds to a plurality ofneurons. Hence, by combining neurons that have output values exceedingthe reference value on the basis of the distances between neighboringneurons that have output values exceeding the reference value, thenumber of faces in an image can be calculated. Also, the average orbarycentric position of the plurality of neurons which are combined inthis way is used as the position of the face.

The rotation amount and face size are calculated as follows. Asdescribed above, the detection results of eyes and a mouth are obtainedas intermediate processing results upon detection of face features. Thatis, as shown in FIG. 10 described in the first embodiment, the eyesearch ranges (RE3, RE4) and mouth search range (RM2) are set using theface detection results, and eye and mouth features can be detected fromthe eye and mouth feature detection results within these ranges. Morespecifically, the average or barycentric positions of a plurality ofneurons that have output values exceeding the reference value of thoseof the eye and mouth detection layers are determined as the positions ofthe eyes (right and left eyes) and mouth. The face size and rotationamount can be calculated from the positional relationship among thesethree points. Upon calculating the face size and rotation amount, onlythe positions of the two eyes may be calculated from the eye featuredetection results, and the face size and rotation amount can becalculated from only the positions of the two eyes without using anymouth feature.

The personal identification unit 1304 executes a determination processfor determining a person who has a face included in each face region inthe photographed image using the face region information and theintermediate detection result information held in the intermediatedetection result holding unit 1303 (step S1404).

The determination process (personal identification process) executed bythe personal identification unit 1304 will be described below. In thefollowing description, a feature vector used in this determinationprocess will be explained first, and identifiers used to identify usingthe feature vector will then be explained.

As has been explained in the background art, the personal identificationprocess is normally executed independently of the face detection processthat detects the face position and size in an image. That is, normally,a process for calculating a feature vector used in the personalidentification process, and the face detection process are independentfrom each other. By contrast, since this embodiment calculates a featurevector used in the personal identification process from the intermediateprocess results of the face detection process, and the number of featureamounts to be calculated during the personal identification process canbe smaller than that in the conventional method, the entire process canbecome simpler.

FIG. 15A shows a feature vector 1301 used in the personal identificationprocess, FIG. 15B shows a right-open V-shaped feature detection resultof the secondary feature, FIG. 15C shows a left-open V-shaped featuredetection result, and FIG. 15D shows a photographed image including aface region.

The dotted lines in FIGS. 15B and 15C indicate eye edges in a face.These edges are not actual feature vectors but are presented to makeeasier to understand the relationship between the V-shaped featuredetection results and eyes. Also, reference numerals 1502 a to 1502 d inFIG. 15B denote firing distributions of neurons in respective featuresin the right-open V-shaped feature detection result of the secondaryfeature: each black mark indicates a large value, and each white markindicate a small value. Likewise, reference numerals 1503 a to 1503 d inFIG. 15C denote firing distributions of neurons in respective featuresin the left-open V-shaped feature detection result of the secondaryfeature: each black mark indicates a large value, and each white markindicate a small value.

In general, in case of a feature having an average shape to be detected,a neuron assumes a large output value. If the shape suffers anyvariation such as rotation, movement, or the like, a neuron assumes asmall output value. Hence, the distributions of the output values ofneurons shown in FIGS. 15B and 15C become weak from the coordinatepositions where the objects to be detected are present toward theperiphery.

As depicted in FIG. 15A, a feature vector 1501 used in the personalidentification process is generated from the right- and left-openV-shaped feature detection results of the secondary features as ones ofthe intermediate detection results held in the intermediate detectionresult holding unit 1303. This feature vector uses a region 1504including the two eyes in place of an entire face region 1505 shown inFIG. 15D. More specifically, a plurality of output values of right-openV-shaped feature detection layer neurons and those of left-open V-shapedfeature detection layer neurons are considered as sequences, and largervalues are selected by comparing output values at identical coordinatepositions, thus generating a feature vector.

In the Eigenface method described in the background art, the entire faceregion is decomposed by bases called eigenfaces, and their coefficientsare set as a feature vector used in personal identification. That is,the Eigenface method performs personal identification using the entireface region. However, if features that indicate different tendenciesamong persons are used, personal identification can be made withoutusing the entire face region. The right- and left-open V-shaped featuredetection results of the region including the two eyes shown in FIG. 15Dinclude information such as the sizes of the eyes, the distance betweenthe two eyes, and the distances between the brows and eyes, and personalidentification can be made based on these pieces of information.

The Eigenface method has a disadvantage, i.e., it is susceptible tovariations of illumination conditions. However, the right- and left-openV-shaped feature detection results shown in FIGS. 15B and 15C areobtained using receptive fields which are learned to detect a face so asto be robust against the illumination conditions and size/rotationvariations, and are prone to the influences of the illuminationconditions and size/rotation variations. Hence, these features aresuited to generate a feature vector used in personal identification.

Furthermore, a feature vector used in personal identification can begenerated by a very simple process from the right- and left-openV-shaped feature detection results, as described above. As describedabove, it is very effective to generate a feature vector used inpersonal identification using the intermediate processing resultsobtained during the face detection process.

In this embodiment, an identifier used to perform personalidentification using the obtained feature vector is not particularlylimited. For example, a nearest neighbor identifier may be used. Thenearest neighbor identifier is a scheme for storing a training vectorindicating each person as a prototype, and identifying an object by aclass to which a prototype nearest to the input feature vector. That is,the feature vectors of respective persons are calculated and held inadvance by the aforementioned method, and the distances between thefeature vector calculated from an input image and the held featurevectors are calculated, and a person who exhibits a feature vector withthe nearest distance is output as an identification result.

As another identifier, a Support Vector Machine (to be abbreviated asSVM hereinafter) proposed by Vapnik et. al. may be used. This SVM learnsparameters of linear threshold elements using maximization of a marginas a reference.

Also, the SVM is an identifier with excellent identification performanceby combining non-linear transformations called kernel tricks (Vapnik,“Statistical Learning Theory”, John Wiley & Sons (1998)). That is, theSVM obtains parameters for determination from training data indicatingrespective persons, and determines a person based on the parameters anda feature vector calculated from an input image. Since the SVM basicallyforms an identifier that identifies two classes, a plurality of SVMs arecombined to perform determination upon determining a plurality ofpersons.

The face detection process executed in step S1402 uses the neuralnetwork that performs image recognition by parallel hierarchicalprocesses, as described above. Also, the receptive fields used upondetecting respective features are acquired by learning using a largenumber of face images and non-face images. That is, the neural networkthat implements a face detection process extracts information, which iscommon to a large number of face images but is not common to non-faceimages, from an input image, and discriminates a face and non-face usingthat information.

By contrast, an identifier that performs personal identification isdesigned to identify a difference of feature vectors generated forrespective persons from face images. That is, when a plurality of faceimages which have slightly different expressions, directions, and thelike are prepared for each person, and are used as training data, acluster is formed for each person, and the SVM can acquire a plane thatseparates clusters with high accuracy if it is used.

There is a rationale that the nearest neighbor identifier can attain anerror probability twice or less the Bayes error probability if it isgiven a sufficient number of prototypes, thus identifying a personaldifference.

FIG. 16 shows, as a table, data used upon learning in three identifiers.That is, the table shown in FIG. 16 shows data used upon training a facedetection identifier to detect faces of persons (including Mr. A and Mr.B), data used upon training a Mr. A identifier to identify Mr. A, anddata used upon training a Mr. B identifier to identify Mr. B. Upontrailing for face detection using the face detection identifier, featurevectors obtained from images of faces of all persons (Mr. A, Mr. B, andother persons) used as samples are used as correct answer data, andbackground images (non-face images) which are not images of faces areused as wrong answer data.

On the other hand, upon training for identification of Mr. A using theMr. A identifier, feature vectors obtained from face images of Mr. A areused as correct answer data, and feature vectors obtained from faceimages of persons other than Mr. A (in FIG. 16, “Mr. B”, “other”) areused as wrong answer data. Background images are not used upon training.

Likewise, upon training for identification of Mr. B using the Mr. Bidentifier, feature vectors obtained from face images of Mr. B are usedas correct answer data, and feature vectors obtained from face images ofpersons other than Mr. B (in FIG. 16, “Mr. A”, “other”) are used aswrong answer data. Background images are not used upon training.

Therefore, although some of the secondary feature detection results usedupon detecting eyes as tertiary features are common to those used inpersonal identification, the identifier (neural network) used to detecteye features upon face detection and the identifier used to performpersonal identification not only are of different types, but also usedifferent data sets used in training. Therefore, even when commondetection results are used, information, which is extracted from theseresults and is used in identification, is consequently different: theformer identifier detects eyes, and the latter identifier determines aperson.

When the face size and direction obtained by the face detection unit1302 fall outside predetermined ranges upon generation of a featurevector, the intermediate processing results held in the intermediatedetection result holding unit 1303 can undergo rotation correction andsize normalization. Since the identifier for personal identification isdesigned to identify a slight personal difference, the accuracy can beimproved when the size and rotation are normalized. The rotationcorrection and size normalization can be done when the intermediateprocessing results held in the intermediate detection result holdingunit 1303 are read out from the intermediate detection result holdingunit 1303 to be input to the personal identification unit 1304.

With the above processes, since personal identification of a face isattained, the personal identification unit 1304 checks if a code (a codeunique to each person) corresponding to the determined person matches acode corresponding to a person who is set in advance (step S1405). Thiscode may be a number, and its expression method is not particularlylimited. This checking result is sent to the control unit 1301.

If the person who is identified by the processes until step S1404matches a specific person who is set in advance, for example, in thisembodiment, if the “code indicating the person” output from the personalidentification unit 1304 matches the code indicating the specific personwho is set in advance, the control unit 1301 records the photographedimage held by the image holding unit 1305 in the recording unit 1307.When the recording unit 1307 comprises a DVD-RAM or compact flash(registered trademark), the control unit 1301 controls the recordingunit 1307 to record the photographed image on a storage media such as aDVD-RAM, compact flash (registered trademark), or the like (step S1406).An image to be recorded may be an image of the face region.

On the other hand, if the person identified by the processes until stepS1404 does not match the specific person who is set in advance, forexample, in this embodiment, if the “code indicating the person” outputfrom the personal identification unit 1304 does not match the codeindicating the specific person who is set in advance, the control unit1301 controls the image sensing unit 1300 to photograph a new image.

In addition, if the identified person matches the specific expression,the control unit 1301 may hold the photographed image on the recordingunit 1307 while controlling the image sensing unit 1300 to sense thenext image in step S1406. Also, the control unit 1301 may control thedisplay unit 1306 to display the photographed image on the display unit1306.

Also, in order to finely sense a face region detection in step S202, thecontrol unit 1301 may set photographing parameters (image sensingparameters of an image sensing system such as exposure correction,auto-focus, color correction, and the like) to perform photographingagain, and to display and record another image.

As described above, when a face in an image is detected on the basis ofthe algorithm that detects a final object to be detected fromhierarchically detected local features, not only processes such asexposure correction, auto-focus, color correction, and the like can bedone based on the detected face region, but also a person can beidentified using the detection results of eye and mouth candidates asthe intermediate feature detection results obtained during the facedetection process without any new detection process for detecting theeyes and mouth. Hence, a person can be detected and photographed whilesuppressing an increase in processing cost. Also, personal recognitionrobust against variations of the face position, size, and the like canbe realized.

The image processing apparatus according to this embodiment may adopt acomputer which comprises the arrangement shown in FIG. 11. Also, theimage processing apparatus according to this embodiment may be appliedto the image processing apparatus 5111 in the image sensing apparatusshown in FIG. 12. In this case, photographing can be made in accordancewith the personal identification result.

Fifth Embodiment

An image processing apparatus according to this embodiment performs theface region detection process described in the above embodiments, theexpression determination process described in the first to thirdembodiments, and the personal identification process described in thefourth embodiment for one image.

FIG. 17 is a block diagram showing the functional arrangement of theimage processing apparatus according to this embodiment. Basically, theimage processing apparatus according to this embodiment has anarrangement obtained by adding that of the image processing apparatus ofthe fourth embodiment and an integration unit 1708 to that of the imageprocessing apparatus according to the first embodiment. Respective unitsexcept for the integration unit 1708 perform the same operations asthose of the units with the same names in the above embodiments. Thatis, an image from an image sensing unit 1700 is output to a facedetection unit 1702, image holding unit 1705, recording unit 1707, anddisplay unit 1706. The face detection unit 1702 executes the same faceregion detection process as in the above embodiments, and outputs thedetection processing result to an expression determination unit 1704 andpersonal identification unit 1714 as in the above embodiments. Also, theface detection unit 1702 outputs intermediate detection results obtainedduring its process to an intermediate detection result holding unit1703. The expression determination unit 1704 executes the same processas in the expression determination unit 104 in the first embodiment. Thepersonal identification unit 1714 executes the same process as in thepersonal identification unit 1304 in the fourth embodiment.

The integration unit 1708 receives data of the processing results of theface detection unit 1702, expression determination unit 1704, andpersonal identification unit 1714, and executes, using these data,determination processes for determining if a face detected by the facedetection unit 1702 is that of a specific person, and if the specificface has a specific expression when it is determined the face is that ofthe specific person. That is, the integration unit 1708 determines if aspecific person has a specific expression.

The main process for identifying a person who has a face in aphotographed image, and determining an expression of that face, which isexecuted by the operations of the above units, will be described belowusing FIG. 18 that shows the flowchart of this process.

Processes in steps S1801 to S1803 are the same as those in steps S1401to S1403 in FIG. 14, and a description thereof will be omitted. That is,in the processes in steps S1801 to S1803, a control unit 1701 and theface detection unit 1702 determine if an image from the image sensingunit 1700 includes a face region.

If a face region is included, the flow advances to step S1804 to executethe same process as that in step S204 in FIG. 2, so that the expressiondetermination unit 1704 determines an expression of a face in thedetected face region.

In step S1805, the same process as that in step S1404 in FIG. 14 isexecuted, and the personal identification unit 1714 identifies a personwith the face in the detected face region.

Note that the processes in steps S1804 and S1805 are executed for eachface detected in step S1802.

In step S1806, the integration unit 1708 manages a “code according tothe determined expression” output from the expression determination unit1704 and a “code according to the identified person” output from thepersonal identification unit 1714 for each face.

FIG. 19 shows an example of the configuration of the managed data. Asdescribed above, the expression determination unit 1704 and personalidentification unit 1714 perform expression determination and personalidentification for each face detected by the face detection unit 1702.Therefore, the integration unit 1708 manages “codes according todetermined expressions” and “code according to identified persons” inassociation with IDs (numerals 1, 2, . . . in FIG. 19) unique to faces.For example, a code “smile” as the “code according to the determinedexpression” and a code “A” as the “code according to the identifiedperson” correspond to a face with an ID=1, and these codes are managedin association with the ID=1. The same applies to an ID=2. In this way,the integration unit 1708 generates and holds table data (with theconfiguration shown in, e.g., FIG. 19) used to manage respective codes.

After that, the integration unit 1708 checks in step S1806 withreference to this table data if a specific person has a specificexpression. For example, whether or not Mr. A is smiling is checkedusing the table data shown in FIG. 19. Since the table data in FIG. 19indicates that Mr. A has a smile, it is determined that Mr. A issmiling.

If a specific person has a specific expression as a result of suchdetermination, the integration unit 1708 advises the control unit 1701accordingly. Hence, the flow advances to step S1807 to execute the sameprocess as in step S1406 in FIG. 14.

In this embodiment, the face detection process and expressiondetermination process are successively done. Alternatively, the methoddescribed in the second and third embodiments may be used. In this case,the total processing time can be shortened.

As described above, according to this embodiment, since a face isdetected from an image, a person is specified, and his or her expressionis specified, a photograph of a desired person with a desired expressioncan be taken among a large number of persons. For example, an instanceof one's child with a smile can be photographed among a plurality ofchildren.

That is, when the image processing apparatus according to thisembodiment is applied to the image processing apparatus of the imagesensing apparatus described in the first embodiment, both the personalidentification process and expression determination process can beexecuted. As a result, a specific person with a specific expression canbe photographed. Furthermore, by recognizing a specific person andexpression, the apparatus can be used as a man-machine interface.

Sixth Embodiment

This embodiment sequentially executes the expression determinationprocess and personal identification process explained in the fifthembodiment. With these processes, a specific expression of a specificperson can be determined accurately.

FIG. 20 is a block diagram showing the functional arrangement of animage processing apparatus according to this embodiment. The arrangementshown in FIG. 20 is substantially the same as that of the imageprocessing apparatus according to the fifth embodiment shown in FIG. 18,except that a personal identification unit 2014 and expressiondetermination unit 2004 are connected, and an expression determinationdata holding unit 2008 is used in place of the integration unit 1708.

FIG. 21 is a flowchart of a main process to be executed by the imageprocessing apparatus according to this embodiment. The process to beexecuted by the image processing apparatus according to this embodimentwill be described below using FIG. 21.

Processes in steps S2101 to S2103 are the same as those in steps S1801to S1803 in FIG. 18, and a description thereof will be omitted.

In step S2104, the personal identification unit 2014 executes a personalidentification process by executing the same process as that in stepS1804. Note that the process in step S2104 is executed for each facedetected in step S1802. In step S2105, the personal identification unit2014 checks if the face identified in step S2104 matches a specificface. This process is attained by referring to management information (atable that stores IDs unique to respective faces and codes indicatingpersons in association with each other), as has been explained in thefifth embodiment.

If a code that indicates the specific face matches a code that indicatesthe identified face, i.e., if the face identified in step S2104 matchesthe specific face, the personal identification unit 2014 advises theexpression determination unit 2004 accordingly, and the flow advances tostep S2106. In step S2106, the expression determination unit 2004executes an expression determination process as in the first embodiment.In this embodiment, the expression determination unit 2004 uses“expression determination data corresponding to each person” held in theexpression determination data holding unit 2008 in the expressiondetermination process.

FIG. 22 shows an example of the configuration of this expressiondetermination data. As shown in FIG. 22, expression determinationparameters are prepared in advance in correspondence with respectivepersons. Note that the parameters include “shadows on the cheeks”,“shadows under the eyes”, and the like in addition to “the distancesfrom the end points of the eyes to the end points of the mouth”, “thehorizontal width of the mouth”, and “the horizontal widths of the eyes”explained in the first embodiment. Basically, as has been explained inthe first embodiment, expression recognition independent from a personcan be made based on a difference from reference data generated fromemotionless image data, but highly precise expression determination canbe done by detecting specific changes depending on a person.

For example, assume that when a specific person smiles, the mouthlargely stretches horizontally, and shadows appear on the cheeks andunder the eyes. In expression determination for that person, thesespecific changes are used to determine an expression with higherprecision.

Therefore, the expression determination unit 2004 receives the codeindicating the face identified by the personal identification unit 2014,and reads out parameters for expression determination corresponding tothis code from the expression determination data holding unit 2008. Forexample, when the expression determination data has the configurationshown in FIG. 22, if the personal identification unit 2014 identifiesthat a given face in the image is that of Mr. A, and outputs a codeindicating Mr. A to the expression determination unit 2004, theexpression determination unit 2004 reads out parameters (parametersindicating the variation rate of the eye-mouth distance >1.1, cheekregion edge density 3.0 . . . ) corresponding to Mr. A, and executes anexpression determination process using these parameters.

In this way, the expression determination unit 2004 can determine anexpression with higher precision by checking if the variation rate ofeye-mouth distance, cheek region edge density, and the like, which areobtained by executing the process described in the first embodiment fallwithin the ranges indicated by the readout parameters.

Referring back to FIG. 21, the expression determination unit 2004 checksif the expression determined in step S2106 matches a specificexpression, which is set in advance. This process is attained bychecking if the code indicating the expression determined in step S2106matches a code that indicates the specific expression, which is set inadvance.

If the two codes match, the flow advances to step S2108, and theexpression determination unit 2004 advises the control unit 1701accordingly, thus executing the same process as in step S1406 in FIG.14.

In this manner, after each person is specified, expression recognitionsuited to that person is done, thereby improving the expressionrecognition precision. Since a face is detected from an image to specifya person, and its expression is specified, a photograph of a desiredperson with a desired expression among a large number of persons can betaken. For example, an instance of one's child with a smile can bephotographed among a plurality of children. Furthermore, since aspecific person and expression are recognized, this apparatus can beused as a man-machine interface.

In the above embodiment, the user can set “specific person” and“specific expression” via a predetermined operation unit as needed.Hence, when the user sets them as needed, codes indicating them arechanged accordingly.

With the aforementioned arrangement of the present invention,identification of a person who has a face in an image and determinationof an expression of that face can be easily made.

Also, variations of the position and direction of an object can be copedwith by a simple method in detection of a face in an image, expressiondetermination, and personal identification.

Seventh Embodiment

Assume that an image processing apparatus according to this embodimenthas the same basic arrangement as that shown in FIG. 11.

FIG. 23 is a block diagram showing the functional arrangement of theimage processing apparatus according to this embodiment.

The functional arrangement of the image processing apparatus comprisesan image input unit for time-serially, successively inputting aplurality of images, a feature amount calculation unit 6101 forextracting feature amounts required to determine an expression from theimages (input images) input by the image input unit 6100, a referencefeature holding unit 6102 for extracting and holding reference featuresrequired to recognize an expression from a reference face as a soberface (emotionless), which is prepared in advance, a feature amountchange amount calculation unit 6103 for calculating change amounts ofrespective feature amounts of a face from the reference face bycalculating differences between the feature amounts extracted by thefeature amount calculation unit 6101 and those held in the referencefeature holding unit 6102, a score calculation unit 6104 for calculatingscores for respective features on the basis of the change amounts of therespective features extracted by the feature amount change amountcalculation unit 6103, and an expression determination unit 6105 fordetermining an expression of the face in the input images on the basisof the sum total of scores calculated by the score calculation unit6104.

Note that the respective units shown in FIG. 23 may be implemented byhardware. However, in this embodiment, the image input unit 6100,feature amount calculation unit 6101, feature amount change amountcalculation unit 6103, score calculation unit 6104, and expressiondetermination unit 6105 are implemented by programs, which are stored inthe RAM 1002. When the CPU 1001 executes these programs, the functionsof the respective units are implemented. The reference feature holdingunit 6102 is a predetermined area assured in the RAM 1002, but may be anarea in the external storage device 1007.

The respective units shown in FIG. 23 will be described in more detailbelow.

The image input unit 6100 inputs time-series face images obtained byextracting a moving image captured by a video camera or the like frameby frame as input images. That is, according to the arrangement shown inFIG. 11, data of images of respective frames are sequentially outputfrom the image sensing unit 100 such as a video camera or the like,which is connected to the I/F 1009 to the RAM 1009 via this I/F 1009.

The feature amount calculation unit 6101 comprises an eye/mouth/noseposition extraction section 6110, edge image generation section 6111,face feature edge extraction section 6112, face feature point extractionsection 6113, and expression feature amount extraction section 6114, asshown in FIG. 24. FIG. 24 is a block diagram showing the functionalarrangement of the feature amount calculation unit 6101.

The respective sections shown in FIG. 24 will be described in moredetail below.

The eye/mouth/nose position extraction section 6110 determinespredetermined portions of a face, i.e., the positions of eyes, a mouth,and a nose (those in the input images) from the images (input images)input by the image input unit 6100. As a method of determining thepositions of the eyes and mouth, for example, the following method maybe used. Templates of the eyes, mouth, and nose are prepared, and eye,mouth, and nose candidates are extracted by template matching. Afterextraction, the eye, mouth, and nose positions are detected using thespatial positional relationship among the eye, mouth, and nosecandidates obtained by template matching, and flesh color information ascolor information. The detected eye and mouth position data are outputto the next face feature edge extraction section 6112.

The edge image generation section 6111 extracts edges from the inputimages obtained by the image input unit 6100, and generates an edgeimage by applying an edge dilation process to the extracted edges andthen applying a thinning process. For example, the edge extraction canadopt edge extraction using a Sobel filter, the edge dilation processcan adopt an 8-neighbor dilation process, and the thinning process canadopt the Hilditch's thinning process. The edge dilation process andthinning process aim at allowing smooth edge scan and feature pointextraction (to be described later) since divided edges are joined bydilating edges and then undergo the thinning process. The generated edgeimage is output to the next face feature edge extraction section 6112.

The face feature edge extraction section 6112 determines an eye region,cheek region, and mouth region in the edge image, as shown in FIG. 25,using the eye and mouth position data detected by the eye/mouth/noseposition extraction unit 6110 and the edge image generated by the edgeimage generation section 6111.

The eye region is set to include only the edges of brows and eyes, thecheek region is set to include only the edges of cheeks and a nose, andthe mouth region is designated to include only an upper lip edge, toothedge, and lower lip edge.

An example of a setting process of these regions will be describedbelow.

As for the height of the eye region, a range which extends upward adistance 0.5 times the distance between the right and left eye positiondetection results and downward a distance 0.3 times the distance betweenthe right and left eye position detection results from a middle pointbetween the right and left eye position detection results obtained fromtemplate matching and the spatial positional relationship is set as avertical range of the eyes.

As for the width of the eye region, a range which extends to the rightand left by the distance between the right and left eye positiondetection results from the middle point between the right and left eyeposition detection results obtained from template matching and thespatial positional relationship is set as a horizontal range of theeyes.

That is, the length of the vertical side of the eye region is 0.8 timesthe distance between the right and left eye position detection results,and the length of the horizontal side is twice the distance between theright and left eye position detection results.

As for the height of the mouth region, a range which extends upward adistance 0.75 times the distance between the nose and mouth positiondetection results and downward a distance 0.25 times the distancebetween the middle point of the right and left eye position detectionresults and the mouth position detection result from the position of themouth position detection result obtained from template matching and thespatial positional relationship is set as a vertical range. As for thewidth of the mouth region, a range which extends to the right and left adistance 0.8 times the distance between the right and left eye positiondetection results from the position of the mouth position detectionresult obtained from template matching and the spatial positionalrelationship is set as a horizontal range of the eyes.

As for the height of the cheek region, a range which extends upward anddownward a distance 0.25 times the distance between the middle pointbetween the right and left eye detection results and the mouth positiondetection result from a middle point (which is a point near the centerof the face) between the middle point between the right and left eyedetection results and the mouth position detection result obtained fromtemplate matching and the spatial positional relationship is set as avertical range.

As for the width of the cheek region, a range which extends to the rightand left a distance 0.6 times the distance between the right and lefteye detection results from the middle point (which is a point near thecenter of the face) between the middle point between the right and lefteye detection results and the mouth position detection result obtainedfrom template matching and the spatial positional relationship is set asa horizontal range of the cheeks.

That is, the length of the vertical side of the cheek region is 0.5times the distance between the middle point between the right and lefteye detection results and the mouth position detection result, and thelength of the horizontal side is 1.2 times the distance between theright and left eye detection results.

With the aforementioned region setting process, as shown in FIG. 25,uppermost edges 6120 and 6121 are determined as brow edges, and seconduppermost edges 6122 and 6123 are determined as eye edges in the eyeregion. In the mouth region, when the mouth is closed, an uppermost edge6126 is determined as an upper lip edge, and a second uppermost edge6127 is determined as a lower lip edge, as shown in FIG. 25. When themouth is open, the uppermost edge is determined as an upper lip edge,the second uppermost edge is determined as a tooth edge, and the thirduppermost edge is determined as a lower lip edge.

The aforementioned determination results are generated by the facefeature edge extraction section 6122 as data identifying the above threeregions (eye, cheek, and mouth regions), i.e., the eye, cheek, and mouthregions, and position and size data of the respective regions, and areoutput to the face feature point extraction section 6113 together withthe edge image.

The face feature point extraction section 6113 detects feature points(to be described later) by scanning the edges in the eye, cheek, andmouth regions in the edge image using various data input from the facefeature edge extraction section 6112.

FIG. 26 shows respective feature points to be detected by the facefeature point extraction section 6113. As shown in FIG. 26, respectivefeature points indicate end points of each edge, and a middle pointbetween the end point on that edge. For example, the end points of anedge can be obtained by calculating the maximum and minimum values ofcoordinate positions in the horizontal direction with reference to thevalues of pixels which form the edge (the value of a pixel which formsthe edge is 1, and that of a pixel which does not form the edge is 0).The middle point between the end points on the edge can be calculated bysimply detecting a position that assumes a coordinate value in thehorizontal direction of the middle point between the end points on theedge.

The face feature point extraction section 6113 obtains the positioninformation these end points as feature point information, and outputseye feature point information (position information of the featurepoints of respective edges in the eye region) and mouth feature pointinformation (position information of the feature points of respectiveedges in the mouth region) to the next expression feature amountextraction section 6114 together with the edge image.

As for feature points, templates for calculating the end point positionsof the eyes, mouth, and nose or the like may be used in the same manneras in position detection of the eyes, mouth, and nose, and the presentinvention is not limited to feature point extraction by means of edgescan.

The expression feature amount extraction section 6114 calculates featureamounts such as a “forehead-around edge density”, “brow edge shapes”,“distance between right and left brow edges”, “distances between browand eye edges”, “distances between eye and mouth end points”, “eye lineedge length”, “eye line edge shape”, “cheek-around edge density”, “mouthline edge length”, “mouth line edge shape”, and the like, which arerequired for expression determination, from the respective pieces offeature point information calculated by the face feature pointextraction section 6113.

Note that the “distances between eye and mouth end points” indicate avertical distance from the coordinate position of a feature point 6136(the right end point of the right eye) to that of a feature point 6147(the right end point of lips), and also a vertical distance from thecoordinate position of a feature point 6141 (the left end point of theleft eye) to that of a feature point 6149 (the left end point of lips)in FIG. 26.

The “eye line edge length” indicates a horizontal distance from thecoordinate position of the feature point 6136 (the right end point ofthe right eye) to that of a feature point 6138 (the left end point ofthe right eye) or a horizontal distance from the coordinate position ofa feature point 6139 (the right end point of the left eye) to that ofthe feature point 6141 (the left end point of the left eye) in FIG. 26.

As for the “eye line edge shape”, as shown in FIG. 27, a line segment(straight line) 6150 specified by the feature point 6136 (the right endpoint of the right eye) and a feature point 6137 (a middle point of theright eye) and a line segment (straight line) 6151 specified by thefeature point 6137 (the middle point of the right eye) and the featurepoint 6138 (the left end point of the right eye) are calculated, and theshape is determined based on the slopes of the two calculated straightlines 6150 and 6151.

The same applies to a process for calculating the shape of the line edgeof the left eye, except for feature points used. That is, the slope of aline segment specified by two feature points (the right end point and amiddle of the left eye) and that of another line segment specified bytwo feature points (the middle point and left end point of the left eye)are calculated, and the shape is similarly determined based on theseslopes.

The “cheek-around edge density” represents the number of pixels whichform edges in the cheek region. Since “wrinkles” are formed when cheekmuscles are lifted up, and various edges having different lengths andwidths are generated accordingly, the number of pixels (that of pixelswith a pixel value=1) which form these edges is counted as an amount ofthese edges, and a density can be calculated by dividing the count valueby the number of images which form the cheek region.

The “mouth line edge length” indicates a distance between the coordinatepoints of two feature points (the right and left end points of themouth) when all the edges in the mouth region are scanned, and a pixelwhich has the smallest coordinate position in the horizontal directionis defined as one feature point (the right end point of the mouth) and apixel with the largest coordinate position is defined as the otherfeature point (the left end point of the mouth).

As described above, the distance between the end points, the slope of aline segment specified by the two end points, and the edge density arecalculated to obtain feature amounts. In other words, this processcalculates feature amounts such as the edge lengths, shapes, and thelike of respective portions. Therefore, these edge lengths and shapeswill often be generally referred to as “edge feature amounts”hereinafter.

The feature amount calculation unit 6101 can calculate respectivefeature amounts from the input images in this way.

Referring back to FIG. 23, the reference feature holding unit 6102 holdsfeature amounts of an emotionless face as a sober face, which aredetected by the feature amount detection process executed by the featureamount calculation unit 6101 from the emotionless face, prior to theexpression determination process.

Hence, in the processes to be described below, change amounts of thefeature amounts detected by the feature amount calculation unit 6101from the edge image of the input images by the feature amount detectionprocess from those held by the reference feature holding unit 6102 arecalculated, and an expression of a face in the input images isdetermined in accordance with the change amounts. Therefore, the featureamounts held by the reference feature holding unit 6102 will often bereferred to as “reference feature amounts” hereinafter.

The feature amount change amount calculation unit 6103 calculates thedifferences between the feature amounts which are detected by thefeature amount calculation unit 6101 from the edge image of the inputimages by the feature amount detection process, and those held by thereference feature holding unit 6102. For example, the unit 6103calculates the difference between the “distances between the end pointsof the eyes and mouth” detected by the feature amount calculation unit6101 from the edge image of the input images by the feature amountdetection process, and “distances between the end points of the eyes andmouth” held by the reference feature holding unit 6102, and sets them asthe change amounts of the feature amounts. Calculating such differencesfor respective feature amounts to calculating changes of feature amountsof respective portions.

Upon calculating the differences between the feature amounts which aredetected by the feature amount calculation unit 6101 from the edge imageof the input images by the feature amount detection process, and thoseheld by the reference feature holding unit 6102, a difference betweenidentical features (e.g., the difference between the “distances betweenthe end points of the eyes and mouth” detected by the feature amountcalculation unit 6101 from the edge image of the input images by thefeature amount detection process, and “distances between the end pointsof the eyes and mouth” held by the reference feature holding unit 6102)is calculated. Hence, these feature amounts must be associated with eachother. However, this method is not particularly limited.

Note that the reference feature amounts become largely different forrespective users. In this case, a given user matches this referencefeature amounts but another unit does not match. Hence, the referencefeature holding unit 6102 may hold reference feature amounts of aplurality of users. In such case, before images are input from the imageinput unit 6100, information indicating whose face images are to beinput is input in advance, and the feature amount change amountcalculation unit 6103 determines reference feature amounts based on thisinformation upon execution of its process. Therefore, the differencescan be calculated using the reference feature amounts for respectiveusers, and the precision of the expression determination process to bedescribed later can be further improved.

The reference feature holding unit 6102 may hold feature amounts of anemotionless face, which are detected from an emotionless image of anaverage face by the feature amount detection process executed by thefeature amount calculation unit 6101 in place of the reference featureamounts for respective users.

Data of respective change amounts which are calculated by the featureamount change amount calculation unit 6103 in this way and indicatechanges of feature amounts of respective portions are output to the nextscore calculation unit 6104.

The score calculation unit 6104 calculates a score on the basis of thechange amount of each feature amount, and “weight” which is calculatedin advance and is held in a memory (e.g., the RAM 1002). As for theweight, analysis for personal differences of change amounts for eachportion is made in advance, and an appropriate weight is set for eachfeature amount in accordance with the analysis result.

For example, small weights are set for features with relatively smallchange amounts (e.g., the eye edge length and the like) and featureswith larger personal differences in change amounts (e.g., wrinkles andthe like), and large weights are set for features with smaller personaldifferences in change amounts (e.g., the distances between the endpoints of the eyes and mouth and the like).

FIG. 28 shows a graph which is to be referred to upon calculating ascore from the eye edge length as an example of a feature which has alarge personal difference in its change amount.

The abscissa plots the feature amount change amount (a value normalizedby a feature amount of a reference face), and the ordinate plots thescore. For example, if the change amount of the eye edge length is 0.4,a score=50 points is calculated from the graph. Even when the changeamount of the eye edge length is 1.2, a score=50 points is calculated inthe same manner as in the case of the change amount=0.3. In this way, aweight is set to reduce the score difference even when the changeamounts are largely different due to personal differences.

FIG. 29 shows a graph which is to be referred to upon calculating ascore from the distance between the end points of the eye and mouth as afeature which has a small personal difference in change amounts.

As in FIG. 28, the abscissa plots the feature amount change amount, andthe ordinate plots the score. For example, when the change amount of thelength of the distance between the end points of the eye and mouth is1.1, 50 points are calculated from the graph; when the change amount ofthe length of the distance between the end points of the eye and mouthis 1.3, 55 points are calculated from the graph. That is, a weight isset to increase the score difference when the change amounts are largelydifferent due to personal differences.

That is, the “weight” corresponds to the ratio between the change amountdivision width and score width when the score calculation unit 6104calculates a score. By executing a step of setting weights forrespective feature amounts, personal differences of the feature amountchange amounts are absorbed. Furthermore, expression determination doesnot depend on only one feature to reduce detection errors andnon-detection, thus improving the expression determination (recognition)ratio.

Note that the RAM 1002 holds data of the graphs shown in FIGS. 27 and28, i.e., data indicating the correspondence between the change amountsof feature amounts and scores, and scores are calculated using thesedata.

Data of scores for respective feature amounts calculated by the scorecalculation unit 6104 are output to the next expression determinationunit 6105 together with data indicating the correspondence between thescores and feature amounts.

The RAM 1002 holds the data of the scores for respective feature amountscalculated by the score calculation unit 6104 by the aforementionedprocess in correspondence with respective expressions prior to theexpression determination process.

Therefore, the expression determination unit 6105 determines anexpression by executing:

1. a comparison process between a sum total value of the scores forrespective feature amounts and a predetermined threshold value; and

2. a process for comparing the distribution of the scores for respectivefeature amounts and those of scores for respective feature amounts forrespective expressions.

For example, an expression indicating joy shows features:

1. eyes slant down outwards;

2. cheek muscles are lifted up; and

3. mouth corners are lifted up

Hence, in the distribution of the calculated scores, the scores of “thedistance between the eye and mouth end points”, “cheek-around edgedensity”, and “mouth line edge length” are very high, and those of the“eye line edge length” and “eye line edge shape” are higher than thoseof other feature amounts, as shown in FIG. 31. Therefore, this scoredistribution is unique to an expression of joy. Other expressions havesuch unique score distributions. FIG. 31 shows the score distribution ofan expression of joy.

Therefore, the expression determination unit 6105 specifies to which ofthe shapes of the score distributions unique to respective expressionthe shape of the distribution defined by the scores of respectivefeature amounts calculated by the score calculation unit 6104 isclosest, and determines an expression represented by the scoredistribution with the closest shape as an expression to be output as adetermination result.

As a method of searching for the score distribution with the closestshape, for example, the shape of the distribution is parametricallymodeled by mixed Gaussian approximation to determine a similaritybetween the calculated score distribution and those for respectiveexpressions by checking the distance in a parameter space. An expressionindicated by the score distribution with a higher similarity with thecalculated score distribution (the score distribution with a smallerdistance) is determined as a candidate of determination.

Then, the process for checking if the sum total of the scores ofrespective feature amounts calculated by the score calculation unit 6104is equal to or larger than a threshold value is executed. Thiscomparison process is effective to accurately determine a non-expressionscene similar to an expression scene as an expression scene. Therefore,if this sum total value is equal to or larger than the predeterminedthreshold value, the candidate is determined as the finally determinedexpression. On the other hand, if this sum total value is smaller thanthe predetermined threshold value, the candidate is discarded, and it isdetermined that a face in the input images is an emotionless ornon-expression face.

In the comparison process of the shape of the score distribution, if thesimilarity is equal to or smaller than a predetermined value, it may bedetermined at that time that a face in the input images is anemotionless or non-expression face, and the process may end withoutexecuting the comparison process between the sum total value of thescores of respective feature amounts calculated by the score calculationunit 6104 with the threshold value.

FIG. 30 is a flowchart showing a determination process for determiningwhether or not an expression of a face in the input images is a“specific expression” using the scores for respective feature amountscalculated by the score calculation unit 6104.

The expression determination unit 6105 checks if the shape of thedistribution defined by the scores of respective feature amountscalculated by the score calculation unit 6104 is close to that of thescore distribution unique to a specific expression (step S6801). Forexample, if a similarity between the calculated score distribution andthe score distribution of the specific expression is equal to or largerthan a predetermined value, it is determined that “the shape of thedistribution defined by the scores of respective feature amountscalculated by the score calculation unit 6104 is close to that of thescore distribution unique to the specific expression”.

If it is determined in step S6801 that the shape of the calculateddistribution is close to that of the specific expression, the flowadvances to step S6802 to execute a determination process fordetermining if the sum total value of the scores of respective featureamounts calculated by the score calculation unit 6104 is equal to orlarger than the predetermined threshold value (step S6802). If it isdetermined that the sum total value is equal to or larger than thethreshold value, it is determined that the expression of the face in theinput images is the “specific expression”, and that determination resultis output.

On the other hand, if it is determined in step S6801 that the shape ofthe calculated distribution is not close to that of the specificexpression, or if it is determined in step S6802 that the sum totalvalue is smaller than the threshold value, the flow advances to stepS6804 to output data indicating that the input images are non-expressionor emotionless images (step S6804).

In this embodiment, both the comparison process between the sum totalvalue of the scores for respective feature amounts and the predeterminedthreshold value, and the process for comparing the distribution of thescores for respective feature amounts with those of the scores forrespective feature amounts for respective expressions are executed asthe expression determination process. However, the present invention isnot limited to such specific processes, and one of these comparisonprocesses may be executed.

With the above processes, according to this embodiment, since thecomparison process of the score distribution and the comparison processwith the sum total value of the scores are executed, an expression of aface in the input image can be more accurately determined. Also, whetheror not the expression of the face in the input images is a specificexpression can be determined.

Eighth Embodiment

FIG. 32 is a block diagram showing the functional arrangement of animage processing apparatus according to this embodiment. The samereference numerals in FIG. 32 denote the same parts as those in FIG. 23,and a description thereof will be omitted. Note that the basicarrangement of the image processing apparatus according to thisembodiment is the same as that of the seventh embodiment, i.e., thatshown in FIG. 11.

The image processing apparatus according to this embodiment will bedescribed below. As described above, the functional arrangement of theimage processing apparatus according to this embodiment is substantiallythe same as that of the image processing apparatus according to theseventh embodiment, except for an expression determination unit 6165.The expression determination unit 6165 will be described in detailbelow.

FIG. 33 is a block diagram showing the functional arrangement of theexpression determination unit 6165. As shown in FIG. 33, the expressiondetermination unit 6165 comprises an expression probabilitydetermination section 6170 and an expression settlement section 6171.

The expression probability determination section 6170 executes the sameexpression determination process as that in the seventh embodiment usingthe score distribution defined by the scores of respective featureamounts calculated by the score calculation unit 6104, and the sum totalvalue of the scores, and outputs that determination result as an“expression probability determination result”. For example, upondetermining an expression of joy or not, it is determined that “there isa possibility of an expression of joy” from the distribution and sumtotal value of the scores calculated by the score calculation unit 6104in place of determining “expression of joy”.

This possibility determination is done to distinguish a non-expressionscene as a conversation scene from a scene of joy, since feature changesof a face of pronunciations “i” and “e” in the conversation scene as thenon-expression scene are roughly equal to those of a face in a scene ofjoy.

The expression settlement section 6171 determines a specific expressionimage using the expression probability determination result obtained bythe expression probability determination section 6170. FIG. 34 is agraph showing the difference between the sum total of scores and athreshold line while the abscissa plots image numbers uniquely assignedto time-series images, and the ordinate plots the difference between thesum total of scores and threshold line, when a non-expression scene as asober face has changed to a joy expression scene.

FIG. 35 is a graph showing the difference between the sum total ofscores and threshold line in a conversation scene as a non-expressionscene while the abscissa plots image numbers of time-series images, andthe ordinate plots the difference the sum total of scores and thresholdline.

With reference to FIG. 34 that shows a case wherein the emotionlessscene has changed to the joy expression scene, a score change varieslargely from an initial process to an intermediate process, but itbecomes calm after the intermediate process, and finally becomes nearlyconstant. That is, from the initial process to the intermediate processupon changing from the emotionless scene to the joy expression scene,respective portions such as the eyes, mouth, and the like of the facevary abruptly, but variations of respective features of the eyes andmouth become calm from the intermediate process to joy, and they finallycease to vary.

The variation characteristics of respective features of the facesimilarly apply to other expressions. Conversely, with reference to FIG.35 that shows a conversation scene as a non-expression scene, in aconversation scene of pronunciation “i” which involves roughly the samefeature changes of the face (e.g., the eyes and mouth) as those of joy,images whose score exceed the threshold line are present. However, inthe conversation scene of pronunciation “i”, respective features of theface always abruptly vary unlike in the joy expression scene. Hence,even when the score becomes equal to or larger than the threshold line,it tends to be quickly equal to or smaller than the threshold line.

Hence, the expression probability determination section 6170 performsexpression probability determination, and the expression settlementsection 6171 executes a step of settling the expression on the basis ofcontinuity of the expression probability determination results. Hence,the conversation scene can be accurately discriminated from theexpression scene.

In psychovisual studies about perception of facial expression bypersons, the action of a face in expression ventilation, especially, thespeed has a decisive influence on determination of an emotion categoryfrom an expression, as can also be seen from M. Kamachi, V. Bruce, S.Mukaida, J. Gyoba, S. Yoshikawa, and S. Akamatsu, “Dynamic propertiesinfluence the perception of facial expression,” Perception, vol. 30, pp.875-887, July 2001.

The processes to be executed by the expression probability determinationsection 6170 and expression settlement section will be described indetail below.

Assume that the probability determination section 6170 determines “firstexpression” for a given input image (an image of the m-th frame). Thisdetermination result is output to the expression settlement section 6171as a probability determination result. The expression settlement section6171 does not immediately output this determination result, and countsthe number of times of determination of the first expression by theprobability determination section 6170 instead. When the probabilitydetermination section 6170 determines a second expression different fromthe first expression, this count is reset to zero.

The reason why the expression settlement section 6171 does notimmediately output the expression determination result (thedetermination result indicating the first expression) is that thedetermined expression is likely to be indefinite due to various causes,as described above.

The probability determination section 6170 executes expressiondetermination processes for respective input images like an input imageof the (m+1)-th frame, an input image of the (m+2)-th frame, . . . . Ifthe count value of the expression settlement section 6171 has reached n,i.e., if the probability determination section 6170 determines “firstexpression” for all n frames in turn from the m-th frame, the expressionsettlement section 6171 records data indicating that this timing is the“start timing of first expression”, i.e., that the (m+n)-th frame is thestart frame in the RAM 1002, and determines an expression of joy afterthis timing until the probability determination section 6170 determinesa second expression different from the first expression.

As in the explanation using FIG. 34, in the expression scene, thedifference between the score sum total and threshold value ceases tochange for a predetermined period of time, i.e., an identical expressioncontinues for a predetermined period of time. Conversely, when anidentical expression does not continue for a predetermined period oftime, a conversation scene as a non-expression scene is likely to bedetected as in the description using FIG. 35.

Therefore, when a possibility of an identical expression is determinedfor a predetermined period of time (n frames in this case) by theprocess executed by the probability determination section 6170, thatexpression is output as a final determination result. Hence, suchfactors (e.g., a conversation scene as a non-expression scene or thelike) that become disturbance in the expression determination processcan be removed, and more accurate expression determination process canbe done.

FIG. 36 is a flowchart of the process which is executed by theexpression settlement section 6171 determining the start timing of anexpression of joy in images successively input from the image input unit6100.

If the probability determination result of the probability determinationsection 6170 indicates joy (step S6190), the flow advances to stepS6191. If the count value of the expression settlement section 6171 hasreached p (p=4 in FIG. 36 (step S6191), i.e., if the probabilitydetermination result of the probability determination section 6170successively indicates job for p frames, this timing is determined as“start of joy”, and data indicating this (e.g., the current frame numberdata and flag data indicating the start of joy) is recorded in the RAM1002 (step S6192).

With the above process, the start timing (start frame) of an expressionof joy can be specified.

FIG. 37 is a flowchart of the process which is executed by theexpression settlement section 6171 determining the end timing of anexpression of joy in images successively input from the image input unit6100.

The expression settlement section 6171 checks with reference to the flagdata recorded in the RAM 1002 in step S6192 if the expression of joy hasstarted but has not ended yet (step S6200). As will be described later,if the expression of joy ends, this data is rewritten to indicateaccordingly. Hence, whether or not the expression of joy has ended yetcurrently can be determined with reference to this data.

If the expression of joy has ended yet, the flow advances to step S6201.If the expression probability section 6170 determines for q (q=3 in FIG.37) frames that there is no possibility of joy, (i.e., if the countvalue of the expression settlement section 6171 is successively zero forq frames), this timing is determined as “end of joy”, and the flag datais recorded in the RAM 1002 after it is rewritten to “data indicatingthe end of joy” (step S6202).

However, if the expression probability section 6170 does notsuccessively determine in step S6201 for q frames that there is nopossibility of joy (i.e., if the count value of the expressionsettlement section 6171 is not successively zero for q frames), theexpression of the face in the input images is determined to be “joy” asa final expression determination result without any data manipulation.

After the end of the expression of joy, the expression settlementsection 6171 determines the expressions in respective frames from thestart timing to the end timing as “joy”.

In this manner, expression start and end images are determined, and allimages between these two images are determined as expression images.Hence, determination errors of expression determination processes forimages between these two images can be suppressed, and the precision ofthe expression determination process can be improved.

Note that this embodiment has exemplified the process for determining anexpression of “joy”, but the processing contents are basically the sameif this expression is other than “joy”.

Ninth Embodiment

FIG. 38 is a block diagram showing the functional arrangement of animage processing apparatus according to this embodiment. The samereference numerals in FIG. 38 denote parts that make substantially thesame operations as those in FIG. 23, and a description thereof will beomitted. Note that the basic arrangement of the image processingapparatus according to this embodiment is the same as that of theseventh embodiment, i.e., that shown in FIG. 11.

The image processing apparatus according to this embodiment receives oneor more candidates indicating an expressions of a face in the inputimages, and determines which of the input candidates corresponds to theexpression of the expression of the face in the input images.

The image processing apparatus according to this embodiment will bedescribed in more detail below. As described above, the functionalarrangement of the image processing apparatus according to thisembodiment is substantially the same as that of the image processingapparatus according to the seventh embodiment, except for an expressionselection unit 6211, feature amount calculation unit 6212, andexpression determination unit 6105. Therefore, the expression selectionunit 6211, feature amount calculation unit 6212, and expressiondetermination unit 6105 will be described in detail below.

The expression selection unit 6211 inputs one or more expressioncandidates. In order to input candidates, the user may select one ormore expressions using the keyboard 1004 or mouse 1005 on a GUI which isdisplayed on, e.g., the display screen of the display device 1006 and isused to select a plurality of expressions. Note that the selectedresults are output to the feature amount calculation unit 6212 andfeature amount change amount calculation unit 6103 as codes (e.g.,numbers).

The feature amount calculation unit 6212 executes a process forcalculating feature amounts required to recognize the expressionsselected by the expression selection unit 6211 from a face in an imageinput from the image input unit 6100.

The expression determination unit 6105 executes a process fordetermining which of the expressions selected by the expressionselection unit 6211 corresponds to the face in the image input from theimage input unit 6100.

FIG. 39 is a block diagram showing the functional arrangement of thefeature amount calculation unit 6212. Note that the same referencenumerals in FIG. 39 denote the same parts as those in FIG. 24, and adescription thereof will be omitted. Respective sections shown in FIG.39 will be described below.

An expression feature amount extraction section 6224 calculates featureamounts corresponding to the expressions selected by the expressionselection unit 6211 using feature point information obtained by the facefeature point extraction section 6113.

FIG. 40 shows feature amounts corresponding to respective expressions(expressions 1, 2, and 3) selected by the expression selection unit6211. For example, according to FIG. 40, features 1 to 4 must becalculated to recognize expression 1, and features 2 to 5 must becalculated to recognize expression 3.

For example, assuming that the expression selection unit 6211 selects anexpression of joy, six features, i.e., the distance between the eye andmouth end points, eye edge length, eye edge slope, mouth edge length,mouth edge slope, and cheek-around edge density are required torecognize the expression of joy. In this way, expression-dependentfeature amounts are required.

Such table indicating feature amounts required to recognize eachexpression (a table which stores correspondence exemplified in FIG. 40),i.e., a table that stores codes indicating expressions input from theexpression selection unit 6211 and data indicating feature amountsrequired to recognize these expressions in correspondence with eachother, is recorded in advance in the RAM 1002.

As described above, since a code corresponding to each selectedexpression is input from the expression selection unit 6211, the featureamount calculation unit 6212 can specify feature amounts required torecognize the expression corresponding to the code with reference tothis table, and can consequently calculate feature amounts correspondingto the expression selected by the expression selection unit 6211.

Referring back to FIG. 38, the next feature amount change amountcalculation unit 6103 calculates differences between the feature amountscalculated by the feature amount calculation unit 6212 and those held bythe reference feature holding unit 6102, as in the seventh embodiment.

Note that the number and types of feature amounts to be calculated bythe feature amount calculation unit 6212 vary depending on expressions.Therefore, the feature amount change amount calculation unit 6103according to this embodiment reads out feature amounts required torecognize the expression selected by the expression selection unit 6211from the reference feature holding unit 6102 and uses them. The featureamounts required to recognize the expression selected by the expressionselection unit 6211 can be specified with reference to the table used bythe feature amount calculation unit 6212.

Since six features, i.e., the distance between the eye and mouth endpoints, eye edge length, eye edge slope, mouth edge length, mouth edgeslope, and cheek-around edge density are required to recognize theexpression of joy, features similar to these six features are read outfrom the reference feature holding unit 6102 and are used.

Since the feature amount change amount calculation unit 6103 outputs thechange amounts of respective feature amounts, the score calculation unit6104 executes the same process as in the seventh embodiment. In thisembodiment, since a plurality of expressions are often selected by theexpression selection unit 6211, the unit 6103 executes the same scorecalculation process as in the seventh embodiment for each of theselected expressions, and calculates the scores for respective featureamounts for each expression.

FIG. 41 shows a state wherein the scores are calculated based on changeamounts for respective expressions.

The expression determination unit 6105 calculates the sum total value ofthe scores for respective expressions pluralized by the expressionselection unit 6211. An expression corresponding to the highest one ofthe sum total values for respective expressions can be determined as anexpression of a face in the input images.

For example, if an expression of joy of those of joy, grief, anger,surprise, hatred, and fear has the highest score sum total, it isdetermined that the expression is an expression of joy.

10th Embodiment

An image processing apparatus according to this embodiment furtherdetermines a degree of expression in an expression scene when itdetermines the expression of a face in input images. As for the basicarrangement and functional arrangement of the image processing apparatusaccording to this embodiment, those of any of the seventh to ninthembodiments may be applied.

In a method of determining the degree of expression, transition of ascore change or score sum total calculated by the score calculation unitfor an input image which is determined by the expression determinationunit to have a specific expression is referred to.

If the score sum total calculated by the score calculation unit has asmall difference from a threshold value of the sum total of the scores,it is determined that the degree of joy is small. Conversely, if thescore sum total calculated by the score calculation unit has a largedifference from a threshold value of the sum total of the scores, it isdetermined that the degree of joy is large. This method can similarlydetermine the degree of expression for expressions other than theexpression of joy.

11th Embodiment

In the above embodiment, whether or not the eye is closed can bedetermined based on the score of the eye shape calculated by the scorecalculation unit.

FIG. 43 shows the edge of an eye of a reference face, i.e., that of theeye when the eye is open, and FIG. 44 shows the edge of an eye when theeye is closed.

The length of an eye edge 6316 when the eye is closed, which isextracted by the feature amount extraction unit remains the same as thatof an eye edge 6304 of the reference image.

However, upon comparison between the slope of a straight line 6308obtained by connecting feature points 6305 and 6306 of the eye edge 6304when the eye is open in FIG. 43 and that of a straight line 6313obtained by connecting feature points 6310 and 6311 of the eye edge 6316when the eye is closed in FIG. 44, the change amount of the slope of thestraight line becomes negative when the state wherein the eye is openchanges to the state wherein the eye is closed.

Also, upon comparison between the slope of a straight line 6309 obtainedfrom feature points 6306 and 6307 of the eye edge 6304 when the eye isopen in FIG. 43 and that of a straight line 6314 obtained from featurepoints 6311 and 6312 of the eye edge 6316 when the eye is closed in FIG.44, the change amount of the slope of the straight line becomes positivewhen the state wherein the eye is closed changes to the state whereinthe eye is open.

Hence, when the eye edge length remains the same, the absolute values ofthe change amounts of the slopes of the aforementioned two, right andleft straight lines obtained from the eye edge have a predeterminedvalue or more, and these change amounts respectively exhibit negativeand positive changes, it is determined that the eye is more likely to beclosed, and the score to be calculated by the score calculation unit isextremely reduced in correspondence with the change amounts of theslopes of the straight lines.

FIG. 42 is a flowchart of a determination process for determiningwhether or not the eye is closed, on the basis of the scores of the eyeshapes calculated by the score calculation unit.

As described above, whether or not the score corresponding to the eyeshape is equal to or smaller than a threshold value is checked. If thescore is equal to or smaller than the threshold value, it is determinedthat the eye is closed; otherwise, it is determined that the eye is notclosed.

12th Embodiment

FIG. 45 is a block diagram showing the functional arrangement of animage processing apparatus according to this embodiment. The samereference numerals in FIG. 45 denote parts that make substantially thesame operations as those in FIG. 23, and a description thereof will beomitted. Note that the basic arrangement of the image processingapparatus according to this embodiment is the same as that of theseventh embodiment, i.e., that shown in FIG. 11.

A feature amount extraction unit 6701 comprises a nose/eye/mouthposition calculation section 6710, edge image generation section 6711,face feature edge extraction section 6712, face feature point extractionsection 6713, and expression feature amount extraction section 6714, asshown in FIG. 46. FIG. 46 is a block diagram showing the functionalarrangement of the feature amount extraction unit 6701.

A normalized feature change amount calculation unit 6703 calculatesratios between respective features obtained from the feature extractionunit 6701 and those obtained from a reference feature holding unit 6702.Note that feature change amounts calculated by the normalized featurechange amount calculation unit 6703 are a “distance between eye andmouth end points”, “eye edge length”, “eye edge slope”, “mouth edgelength”, and “mouth edge slope” if a smile is to be detected.Furthermore, respective feature amounts are normalized according to facesize and rotation variations.

A method of normalizing the feature change amounts calculated by thenormalized feature change amount calculation unit 6703 will be describedbelow. FIG. 47 shows the barycentric positions of the eyes and nose in aface in an image. Referring to FIG. 47, reference numerals 6720 and 6721respectively denote the barycentric positions of the right and lefteyes; and 6722, the barycentric position of a nose. From the barycentricposition 6722 of the nose and the barycentric positions 6720 and 6721 ofthe eyes, which are detected by the nose/eye/mouth position detectionsection 6710 of the feature amount extraction unit 6701 usingcorresponding templates, a horizontal distance 6730 between the righteye position and face position, a horizontal distance 6731 between theleft eye position and face position, and a vertical distance 6732between the average vertical coordinate position of the right and lefteyes and the face position are calculated, as shown in FIG. 49.

As for a ratio a:b:c of the horizontal distance 6730 between the righteye position and face position, the horizontal distance 6731 between theleft eye position and face position, and the vertical distance 6732between the average vertical coordinate position of the right and lefteyes and the face position, when the face size varies, a ratio a1:b1:c1of a horizontal distance 6733 between the right eye position and faceposition, a horizontal distance 6734 between the left eye position andface position, and a vertical distance 6735 between the average verticalcoordinate position of the right and left eyes and the face positionremains nearly unchanged, as shown in FIG. 50. However, a ratio a:a1 ofthe horizontal distance 6730 between the right eye position and faceposition when the size does not vary and the horizontal distance 6733between the right eye position and face position, a horizontal distance6734 between the left eye position and face position when the sizevaries, changes according to the face size variation. Upon calculatingthe horizontal distance 6730 between the right eye position and faceposition, the horizontal distance 6731 between the left eye position andface position, and the vertical distance 6732 between the averagevertical coordinate position of the right and left eyes and the faceposition, eye end point positions (6723, 6724), right and left nasalcavity positions, and the barycentric position of the right and leftnasal cavity positions may be used in addition to the barycentricpositions of the nose and eyes, as shown in FIG. 48. As a method ofcalculating the eye end points, a method of scanning an edge and amethod using a template for eye end point detection are available. As amethod for calculating the nasal cavity positions, a method of using thebarycentric positions of the right and left nasal cavities or right andleft nasal cavity positions using a template for nasal cavity detectionis available. As the distance between features used to determine avariation, other features such as a distance between right and leftlarmiers and the like may be used.

Furthermore, a ratio c:c2 of the vertical distance 6732 between theaverage vertical coordinate position of the right and left eyes and theface position when the face does not rotate, as shown in FIG. 49 and avertical distance 6738 between the average vertical coordinate positionof the right and left eyes and the face position changes depending onthe up or down rotation of the face, as shown in FIG. 51.

As shown in FIG. 52, a ratio a3:b3 of a horizontal distance 6739 betweenthe right eye position and face position and a horizontal distance 6740between the left eye position and face position changes compared to theratio a:b of the horizontal distance 6730 between the right eye positionand face position and the horizontal distance 6731 between the left eyeposition and face position when the face does not rotate to the right orleft, as shown in FIG. 49.

When the face has rotated to the right or left, a ratio g2/g1 of a ratiog1 (=d1/e1) of a distance d1 between the end points of the right eye anda distance e1 between the end points of the left eye of a referenceimage (an emotionless image) shown in FIG. 53 and a ratio g2 (=d2/e2) ofa distance d2 between the end points of the right eye and a distance e2between the end points of the left eye of an input image (a smilingimage) shown in FIG. 54 may be used.

FIGS. 55A and 55B are flowcharts of the process for determining a sizevariation, right/left rotation variation, and up/down rotationvariation. The process for determining a size variation, right/leftrotation variation, and up/down rotation variation will be describedbelow using the flowcharts of FIGS. 55A and 55B. In this case, FIG. 49is used as a “figure that connects the positions of the eyes and nosevia straight lines while no variation occurs, and FIG. 56 is used as a“figure that connects the positions of the eyes and nose via straightlines after a size variation, right/left rotation variation, or up/downrotation variation has occurred”.

It is checked in step S6770 if a:b:c=a4:b4:c4. Upon checking if “tworatios are equal to each other”, they need not always be “exactly equal”to each other, and it may be determined that they are “equal” if “thedifference between the two ratios falls within a given allowable range”.

If it is determined in the checking process in step S6770 thata:b:c=a4:b4:c4, the flow advances to step S6771 to determine “no changeor size variation only”. Furthermore, the flow advances to step S6772 tocheck if a/a4=1.

If a/a4=1, the flow advances to step S6773 to determine “no size androtation variations”. On the other hand, if it is determined in stepS6772 that a/a4≠1, the flow advances to step S6774 to determine “sizevariation only”.

On the other hand, if it is determined in the checking process in stepS6770 that a:b:c≠a4:b4:c4, the flow advances to step S6775 to determine“any of up/down rotation, right/left rotation, up/down rotation and sizevariation, right/left rotation and size variation, up/down rotation andright/left rotation, and up/down rotation and right/left rotation andsize variation”.

The flow advances to step S6776 to check if a:b=a4:b4 (the process forchecking if “two ratios are equal to each other” in this case is done inthe same manner as in step S6770). If a b=a4:b4, the flow advances tostep S6777 to determine “any of up/down rotation, and up/down rotationand size variation”. The flow advances to step S6778 to check if a/a4=1.If it is determined that a/a4≠1, the flow advances to step S6779 todetermine “up/down rotation and size variation”. On the other hand, ifit is determined that a/a4=1, the flow advances to step S6780 todetermine “up/down rotation only”.

On the other hand, if it is determined in step S6776 that a:b≠a4:b4, theflow advances to step S6781 to check if a/a4=1, as in step S6778.

If a/a4=1, the flow advances to step S6782 to determine “any ofright/left rotation, and up/down rotation and right/left rotation”. Theflow advances to step S6783 to check if c/c3=1. If it is determined thatc/c≠1, the flow advances to step S6784 to determine “up/down rotationand right/left rotation”. If it is determined that c/c3=1, the flowadvances to step S6785 to determine “right/left rotation”.

On the other hand, if it is determined in step S6781 that a/a4≠1, theflow advances to step S6786 to determine “any of right/left rotation andsize variation, and up/down rotation and right/left rotation and sizevariation”. The flow advances to step S6787 to check if (a4/b4)/(a/b)>1.

If (a4/b4)/(a/b)>1, the flow advances to step S6788 to determine “leftrotation”. The flow advances to step S6789 to check if a:c=a4:c4 (thesame “equal” criterion as in step S6770 applies). If a:c=a4:c4, the flowadvances to step S6790 to determine “right/left rotation and sizevariation”. On the other hand, if a:c≠a4:c4, the flow advances to stepS6793 to determine “up/down rotation and right/left rotation and sizevariation”.

On the other hand, if it is determined in step S6787 that(a4/b4)/(a/b)≦1, the flow advances to step S6791 to determine “rightrotation”. The flow advances to step S6792 to check if b:c=b4:c4 (thesame “equal” criterion as in step S6770 applies). If b:c=b4:c4, the flowadvances to step S6790 to determine “right/left rotation and sizevariation”. On the other hand, if b:c≠b4:c4, the flow advances to stepS6793 to determine “up/down rotation and right/left rotation and sizevariation”. The ratios used in respective steps are not limited to thosewritten in the flowcharts. For example, in steps S6772, S6778, andS6781, b/b4, (a+b)/(a4+b4), and the like may be used.

With the above process, the face size and rotation variations can bedetermined. If these variations are determined, respective featurechange amounts calculated by the normalized feature change amountcalculation unit 6703 are normalized, thus allowing recognition of anexpression even when the face size has varied or the face has rotated.

As the feature amount normalization method, for example, a case will beexplained below using FIGS. 49 and 50 wherein only a size variation hastaken place. In such case, all feature change amounts obtained from aninput image need only be multiplied by 1/(a1/a). Note that 1(1b/b),1/((a1+b1)/(a+b)), 1/(c1/c), and other features may be used in place of1/(a1/a). When an up/down rotation and size variation have occurred, asshown in FIG. 57, after the distances between the eye and mouth endpoints, which are influenced by the up/down rotation, multiplied by(a5/c5)/(a/c), all feature amounts can be multiplied by 1/(a1/a). Incase of the up/down rotation, the present invention is not limited touse of (a5/c5)/(a/c) as in the above case. In this way, the face sizevariation, and up/down and right/left rotation variations aredetermined, and the feature change amounts are normalized to allowrecognition of an expression even when the face size has varied or theface has suffered the up/down rotation variation and/or right/leftrotation variation.

FIG. 58 is a flowchart of a process for normalizing feature amounts inaccordance with up/down and right/left rotation variations and sizevariation on the basis of the detected positions of the right and lefteyes and nose, and determining an expression.

After the barycentric coordinate positions of the right and left eyesand nose are detected in step S6870, it is checked in step S6871 ifright/left and up/down rotation variations or a size variation haveoccurred. If neither right/left nor up/down rotation variations haveoccurred, it is determined in step S6872 that normalization of featurechange amounts is not required. The ratios of the feature amounts toreference feature amounts are calculated to calculate change amounts ofthe feature amounts. The scores are calculated for respective featuresin step S6873, and the score sum total are calculated from respectivefeature amount change amounts in step S6874. On the other hand, if it isdetermined in step S6871 that the right/left and up/down rotationvariations or size variation have occurred, it is determined in stepS6875 that normalization of feature change amounts is required. Theratios of the feature amounts to reference feature amounts arecalculated to calculate change amounts of the feature amounts, which arenormalized in accordance with the right/left and up/down rotationvariations or size variation. After that, the scores are calculated forrespective features in step S6873, and the score sum total arecalculated from respective feature amount change amounts in step S6974.

An expression of a face in the input image is determined on the basis ofthe calculated sum total of the scores in the same manner as, in thefirst embodiment in step S6876.

13th Embodiment

FIG. 59 is a block diagram showing the functional arrangement of animage sensing apparatus according to this embodiment. The image sensingapparatus according to this embodiment comprises an image sensing unit6820, image processing unit 6821, and image secondary storage unit 6822,as shown in FIG. 59.

FIG. 60 is a block diagram showing the functional arrangement of theimage sensing unit 6820. As shown in FIG. 60, the image sensing unit6820 roughly comprises an imaging optical system 6830, solid-state imagesensing element 6831, video signal process 6832, and image primarystorage unit 6833.

The imaging optical system 6830 comprises, e.g., a lens, which imagesexternal light on the next solid-state image sensing element 6831, as iswell known. The solid-state image sensing element 6831 comprises, e.g.,a CCD, which converts an image formed by the imaging optical system 6830into an electrical signal, and consequently output a sensed image to thenext video signal processing circuit 6832 as an electrical signal, as iswell known. The video signal processing circuit 6832 A/D-converts thiselectrical signal, and outputs a digital signal to the next imageprimary storage unit 6833. That is, data of a sensed image is output tothe image primary storage unit 6833. The image primary storage unit 6833comprises a storage medium such as a flash memory or the like, andstores data of the sensed image.

FIG. 61 is a block diagram showing the functional arrangement of theimage processing unit 6821. The image processing unit 6821 comprises animage input unit 6840 which reads out sensed image data stored in theimage primary storage unit 6833 and outputs the readout data to a nextfeature amount extraction unit 6842, an expression information inputunit 6841 which receives expression information (to be described later)and outputs it to the next feature amount extraction unit 6842, thefeature amount extraction unit 6842, a reference feature holding unit6843, a change amount calculation unit 6844 which calculates changeamounts by calculating the ratios of feature amounts extracted by thefeature amount extraction unit 6842, a change amount normalization unit6845 which normalizes the change amounts of respective featurescalculated by the change amount calculation unit 6844 in accordance withrotation and up/down variations, or a size variation, a scorecalculation unit 6846 which calculates scores for respective changeamounts from the change amounts of features normalized by the changeamount normalization unit 6845, and an expression determination unit6847. The respective units shown in FIG. 61 have the same functions asthose with the same names which appear in the above embodiments, unlessotherwise specified.

The expression information input unit 6841 inputs photographingexpression information when a photographer selects an expression to bephotographed. That is, when the photographer wants to take a smilingimage, he or she selects a smile photographing mode. In this manner,only a smile is photographed. Hence, this expression informationindicates a selected expression. Note that the number of expressions tobe selected is not limited to one, but a plurality of expressions may beselected.

FIG. 62 is a block diagram showing the functional arrangement of thefeature amount extraction unit 6842. As shown in FIG. 62, the featureamount extraction unit 6842 comprises a nose/eye/mouth positioncalculation section 6850, edge image generation section 6851, facefeature edge extraction section 6852, face feature point extractionsection 6853, and expression feature amount extraction section 6854. Thefunctions of the respective units are the same as those shown in FIG.46, and a description thereof will be omitted.

The image input unit 6840 in the image processing unit 6821 reads outsensed image data stored in the image primary storage unit 6833, andoutputs the readout data to the next feature amount extraction unit6842. The feature amount extraction unit 6842 extracts feature amountsof an expression to be photographed, which is selected by thephotographer, on the basis of expression information input from theexpression information input unit 6841. For example, when thephotographer wants to take a smiling image, the unit 6842 extractsfeature amounts required to recognize a smile.

Furthermore, the change amount calculation unit 6844 calculates changeamounts of respective feature amounts by calculating the ratios betweenthe extracted feature amounts and those which are held by the referencefeature holding unit 6843. The change amount normalization 6845normalizes the ratios of respective feature change amounts calculated bythe change amount calculation unit 6844 in accordance with a face sizevariation or rotation variation. The score calculation unit 6846calculates scores in accordance with weights and change amounts forrespective features.

FIG. 63 is a block diagram showing the functional arrangement of theexpression determination unit 6847. An expression probabilitydetermination section 6860 performs probability determination of anexpression obtained by the expression information input unit 6841 by athreshold process of the sum total of the scores for respective featurescalculated by the score calculation unit 6846. An expression settlementsection 6861 settles an expression obtained by the expressioninformation input unit 6841 on the basis of continuity of expressionprobability determination results. If an input expression matches theexpression obtained by the expression information input unit 6841, imagedata sensed by the image sensing unit 6820 is stored in the imagesecondary storage unit 6822.

In this way, only an image with an expression that the photographerintended can be recorded.

Note that the functional arrangement of the image processing unit 6821is not limited to this, and the apparatus (or program) which isconfigured to execute the expression recognition process in each of theabove embodiments may be applied.

14th Embodiment

FIG. 64 is a block diagram showing the functional arrangement of animage sensing apparatus according to this embodiment. The same referencenumerals in FIG. 64 denote the same parts as those in FIG. 59, and adescription thereof will be omitted. The image sensing apparatusaccording to this embodiment comprises an arrangement to which an imagedisplay unit 6873 is added to the image sensing apparatus according tothe 13th embodiment.

The image display unit 6873 comprises a liquid crystal display or thelike, and displays an image recorded in the image secondary storage unit6822. Note that the image display unit 6873 may display only an imageselected by the photographer using the image processing unit 6821. Thephotographer can select whether or not an image displayed on the imagedisplay unit 6873 is to be stored in the image secondary storage unit6822 or is to be deleted. For this purpose, the image display unit 6873may comprise a touch panel type liquid crystal display, which displays,on its display screen, a menu that prompts the photographer to selectwhether an image displayed on the image display unit 6873 is to bestored in the image secondary storage unit 6822 or is to be deleted, soas to allow the photographer to make one of these choices.

According to the aforementioned arrangement of the present invention, anexpression of a face in an image can be accurately determined by amethod robust against personal differences, expression scenes, and thelike. Furthermore, even when the face size has varied or the face hasrotated, an expression of a face in an image can be determined moreaccurately.

In the above embodiments, an object to be photographed is a face.However, the present invention is not limited to such specific object,and vehicles, buildings, and the like may be photographed.

Other Embodiments

The objects of the present invention are also achieved by supplying arecording medium (or storage medium), which records a program code of asoftware program that can implement the functions of the above-mentionedembodiments to the system or apparatus, and reading out and executingthe program code stored in the recording medium by a computer (or a CPUor MPU) of the system or apparatus. In this case, the program codeitself read out from the recording medium implements novel functions ofthe present invention, and the recording medium which stores the programcode constitutes the present invention.

The functions of the above-mentioned embodiments may be implemented notonly by executing the readout program code by the computer but also bysome or all of actual processing operations executed by an OS or thelike running on the computer on the basis of an instruction of theprogram code.

Furthermore, the functions of the above-mentioned embodiments may beimplemented by some or all of actual processing operations executed by aCPU or the like arranged in a function extension card or a functionextension unit, which is inserted in or connected to the computer, afterthe program code read out from the recording medium is written in amemory of the extension card or unit.

When the present invention is applied to the recording medium, thatrecording medium stores program codes corresponding to theaforementioned flowcharts.

The present invention is not limited to the above embodiments andvarious changes and modifications can be made within the spirit andscope of the present invention. Therefore, to apprise the public of thescope of the present invention, the following claims are made.

CLAIM OF PRIORITY

This application claims priority from Japanese Patent Application No.2003-199357 filed on Jul. 18, 2003, Japanese Patent Application No.2003-199358 filed on Jul. 18, 2003, Japanese Patent Application No.2004-167588 filed on Jun. 4, 2004, and Japanese Patent Application No.2004-167589 filed on Jun. 4, 2004, the entire contents of which areincorporated by reference herein.

The invention claimed is:
 1. An image sensing apparatus comprising: asensing unit constructed to successively sense images; a regiondetermination unit constructed to determine a face region in each of thesuccessively sensed images; an extraction unit constructed to extractpredetermined features representing parts of a face from the determinedface region in each of the successively sensed images; a scorederivation unit constructed to derive a score corresponding to anexpression of the face in each of the successively sensed images on thebasis of the predetermined features extracted from the face region; arecording unit constructed to selectively record the successively sensedimages in a predetermined storage device; and a control unit constructedto control whether or not to perform recording in the predeterminedstorage device for each of the successively sensed images, based on thescore derived for each of the successively sensed images.
 2. An imageprocessing apparatus comprising: a sensing unit constructed tosuccessively sense frame images; a region determination unit constructedto determine a face region in each of the successively sensed frameimages; an extraction unit constructed to extract predetermined featuresrepresenting parts of a face from the determined face region in each ofthe successively sensed frame images; a score derivation unitconstructed to derive a score corresponding to an expression of the faceon the basis of differences between relative positions of predeterminedfeatures detected from a face image which is set in advance as areference and the extracted predetermined features included in a regionof an image of a second frame subsequent to a first frame, wherein theregion of the image of the second frame positionally corresponds to theface region determined by said region determination unit in an image ofthe first frame sensed by said sensing unit; a recording unitconstructed to selectively record the successively sensed frame imagesin a predetermined storage device; and a control unit constructed tocontrol whether or not to perform recording in the predetermined storagedevice for each of the successively sensed frame images, based on thescore derived for each of the successively sensed frame images.
 3. Animage processing apparatus comprising: a sensing unit constructed tosuccessively sense images; a region determination unit constructed todetermine a face region in each of the successively sensed images; anextraction unit constructed to extract predetermined featuresrepresenting parts of a face from the determined face region in each ofthe successively sensed images; a first determination unit constructedto identify a person who has the face in the image sensed by saidsensing unit using the predetermined features extracted from the faceregion determined by said region determination unit; score derivationunit constructed to derive a score corresponding to an expression of theface using the predetermined features extracted from the face regiondetermined by said region determination unit; a recording unitconstructed to selectively record the successively sensed images in apredetermined storage device; and a control unit constructed to controlwhether or not to perform recording in the predetermined storage devicefor each of the successively sensed images, based on the score derivedfor each of the successively sensed images.
 4. An image processingmethod comprising: performing by a processor the following steps: aninput step of successively sensing images; a region determination stepof determining a face region in each of the successively sensed images;an extraction step of extracting predetermined features representingparts of a face from the determined face region in each of thesuccessively sensed images; a score derivation step of deriving a scorecorresponding to an expression of the face in each of the successivelysensed images on the basis of the predetermined features extracted fromthe face region; a recording step of selectively recording thesuccessively sensed images in a predetermined storage device; and acontrol step of controlling whether or not to perform recording in thepredetermined storage device for each of the successively sensed images,based on the score derived for each of the successively sensed images.5. An image processing method comprising: performing by a processor thefollowing steps: an input step of successively sensing frame images; aregion determination step of determining a face region in each of thesuccessively sensed frame images; an extraction step of extractingpredetermined features representing parts of a face from the determinedface region in each of the successively sensed frame images; a scorederivation step of deriving a score corresponding to an expression ofthe face on the basis of differences between relative positions of thepredetermined features detected from a face image which is set inadvance as a reference and the extracted predetermined features includedin a region of an image of a second frame subsequent to a first frame,wherein the region of the image of the second frame positionallycorresponds to the face region determined in said region determinationstep in an image of the first frame sensed in the sensing step; arecording step of selectively recording the successively sensed frameimages in a predetermined storage device; and a control step ofcontrolling whether or not to perform recording in the predeterminedstorage device for each of the successively sensed frame images, basedon the score derived for each of the successively sensed frame images.6. An image processing method comprising: performing by a processor thefollowing steps: a sensing step of successively sensing images; a regiondetermination step of determining a face region in each of thesuccessively sensed images; an extraction step of extractingpredetermined features representing parts of a face from the determinedface region in each of the successively sensed images; a firstdetermination step of identifying a person who has the face in the imagesensed in the sensing step using the predetermined features extractedfrom the face region determined in the region determination step; ascore derivation step of deriving a score corresponding to an expressionof the face using the predetermined features extracted from the faceregion determined in the region determination step; a recording step ofselectively recording the successively sensed images in a predeterminedstorage device; and a controlling step of controlling whether or not toperform recording in the predetermined storage device for each of thesuccessively sensed images, based on the score derived for each of thesuccessively sensed images.
 7. A computer-executable program retrievablystored on a non-transitory computer-readable storage medium for making acomputer execute an image processing method of claim
 4. 8. Anon-transitory computer-readable storage medium retrievably storing aprogram of claim
 7. 9. An image sensing apparatus according to claim 1,further comprising: a first feature detection unit for detecting aplurality of local features from an entirety of the image sensed by saidsensing unit; and a second feature detection unit for detectingpredetermined features representing parts of the face from the entiretyof the sensed image on the basis of combinations of the detected localfeatures, wherein said region determination unit determines the faceregion in the sensed image on the basis of the detected predeterminedfeatures.
 10. An image sensing apparatus according to claim 9, whereinsaid extraction unit extracts predetermined features included within thedetermined predetermined face region from the predetermined features inthe entirety of the sensed image detected by said second featuredetection unit.
 11. An image sensing apparatus according to claim 1,wherein said score derivation unit derives the score corresponding tothe expression of the face on the basis of the differences betweenrelative positions of the predetermined features extracted from the faceregion and predetermined features detected in a face image which is setin advance as a reference.
 12. An image processing apparatus accordingto claim 3, further comprising: a first feature detection unit fordetecting a plurality of local features from an entirety of the imagesensed by said sensing unit; and a second feature detection unit fordetecting predetermined features representing parts of the face from theentirety of the sensed image on the basis of combination of the detectedlocal features, wherein said region determination unit determines theface region in the sensed image on the basis of the detectedpredetermined features.
 13. The apparatus according to claim 9, whereinsaid first feature detection unit, said second feature detection unitand said region determination unit comprise a hierarchical neuralnetwork, and wherein intermediate layer outputs of the hierarchicalneural network are used as detection results for the predeterminedfeatures.
 14. The apparatus according to claim 11, wherein said scorederivation unit derives which distribution, of a number of distributionscorresponding to respective expressions derived in advance, has ahighest similarity to a distribution of differences between relativepositions of the predetermined features extracted from the face regionand relative positions derived in advance as references for thepredetermined features in the face region, and determines an expressionindicated by the distribution with the highest similarity.
 15. Theapparatus according to claim 11, wherein said input unit successivelyinputs images by successively executing a process of inputting a nextimage when said region determination unit completes a process fordetermining a face region, and wherein said score derivation unitexecutes a process for deriving the score corresponding to theexpression of the face on the basis of differences between relativepositions of the predetermined features extracted from the face regionusing an image previously sensed by said sensing unit at a timing whensaid sensing unit inputs an image, and relative positions derived inadvance as references for the predetermined features in the face region.16. An image processing apparatus according to claim 3, wherein saidscore derivation unit changes parameters used to perform expressiondetermination for a face of interest, in according with a result fromfirst determination unit which identifies the person of the face ofinterest.
 17. A non-transitory computer-readable storage mediumretrievably storing a program for making a computer execute an imageprocessing method of claim
 5. 18. A non-transitory computer-readablestorage medium retrievably storing a program for making a computerexecute an image processing method of claim 6.